Sefaria III: Comparative Graphing

In the last few months, Sefaria.org has added several tens of thousands of new links to the site, mostly through judiciously crawling the wikitexts archive. The combination of human-scale translations and data-scale linkings is fascinating – the sheer number of links would take ages for a person to do, but as time progresses, we must be approaching the maximum number of links that can be added algorithmically. I’d mentioned, in a previous post, that there’s a huge difference between collecting exact quotes and subtle allusions. It would be interesting to see what happens to Sefaria as it moves towards discovering the latter.[1]
However, it’s also pretty interesting to see the evolution of the former.

Before I return to the circle of text, special thanks to Josh, who recently got a new desktop and donated his old one to me after putting in a new solid state drive. It has become my official work machine (when not also being used to play Portal. Again.)

So, when last we left Sefaria, they were at 87,000 or so links between texts. By August 25nd, they had over 150,000. By September 22nd, they were up to 300,000. So my first question, as you might imagine, is as follows: assuming that I use the same layout algorithms for each, how do the graphs compare to one another?

Open Ord graphs of May, August and September

These three graphs were all created using the force directed OpenOrd layout algorithm in Gephi, mostly because it’s the only layout other than isometric that can really handle this much data.

Arguably, the first thing we’ve proved is that Alexander Galloway is right in The Interface Effect, “Only one visualization has ever been made of an information network, for there can only be one” (84). Galloway observes that the style and structure of network visualizations all look the same or, more accurately, all use the same aesthetic codes to say the same things and what they tend to say is that this object is big and interconnected—each image fundamentally exists as a symbol of the network without any kinds of representation.

Which leaves me, as a reader and student, with two series of questions. The first—which I am going to hold off on answering until my final post in which I plan to discuss questions of worth—asks what the aesthetic and poetic value of these visualizations are? What, in the strongest sense of the term, do we do with them as objects that speak about an archive or even as objets d’art?[2]

But we’ll get to that later. In the mean time, let us ask a different kind of question. If we take each graph as the symbolic representation of algorithm working on data, how do we use that representation to reorient more traditional forms of inquiry? This is part of the humanities’ continued break with what I think of as the ordinary uses of data visualization wherein the purpose of the image is to convey to the human eye what the algorithm has learned. It’s a method of displaying new knowledge that has been interpreted by virtue of having been computationally mediated. But these visualizations work slightly differently. While their job is still to show us Sefaria’s output in a manner readable to our eyes and brains, they exist as the starting point in humanist inquiry rather than the purpose of it. I see these graphs as pointers, methods of discovering or uncovering areas in the network that are of interest to scholarship. The visualizations, taken separately and together, are a way of telling us to look here.[3] Why is this node different from all other nodes? When we line up these three images, which look more and more like the multicolored Eye of Sauron, how do they draw our attention and what inquiries do they suggest?

With that in mind, here are some questions I thought of:
– What is the node that looks like a lens flare on the right-hand visualization?
– Why do each of this images have a halo?
– Is the largest node the same in each of these images?
– Why do I only remember to check the repository at the end of the month?

So these are questions we can answer, except perhaps that last. I will take on the first two. The lens flare, interestingly enough, is a single chapter from Tanna Debei Eliyahu Rabbah. If you were expecting something a bit more well-known and your response was “who now?” rest assured, you’re not alone. I was surprised too.
Tanna Debei Eliyahu is a work of midrash, which means that it uses biblical verses to craft narratives and make arguments about the nature of the world (rather than, say, use them as prooftexts for legal decisions). David Stern describes it as an “exposition of themes and ideas, but one whose coherent presentation is always being sidetracked by the lure of exegesis” in his book, Parables in Midrash.

So why does its 18th chapter have so many edges stretching out of it?
Here, by the way, is what it looks like when graphed isometrically:

Isometric Graph of the September Dataset

As to why, the simplest answer is because that chapter refers to an astounding 113 different verses in the Bible. Nothing else, as the graph shows us, comes close. The passage in question begins with an exegesis of a verse from Lamentations and can be found here. For context, the other chapters have between 3 and 35 biblical quotations in each section.

So there’s a research project for someone interested in Midrash. Is there something unique about the contents of this chapter that matches the odd data we’ve gleaned about it? Why does this section require so many prooftexts? Are those texts similar across the entire section or is there an evolution of their content? Of course, I’m not in the business of interpreting Midrash, at least not at this point in my career. The rather serendipitous revelations of this research remain as pointers.

So much for micro-level drilling down. Now let’s go back and take this opportunity to look at the evolution of the archive. This graph is a testament to Sefaria’s growth, but more importantly to us, it shows what the database looks like as it approaches a more accurate representation of the interconnectedness of Jewish texts.

What strikes me as interesting, at least in this network, are the regions of growth. The centers become denser and more intricately connected while the halo around the outside remains the same diffuse constellation it had been more or less since the beginning. According to that view, the nodes around the outside remain the same, while new nodes add themselves in to the central cluster. Given the scale of the images versus their pixel density, I went back to Gephi itself to check the compositions of each one.

Here’s the image again, just as a refresher:

Open Ord graphs of May, August and September

In the May graph, the outermost ring is composed of less prominent Biblical verses and their commentaries, along with the occasional Talmudic fragment that comments on them. The inner ring, on the other hand, comprises the Talmudic pages that lack links to anything other than their own commentaries. The August graph works on the same obscurity principle and, despite appearances, the presence of a text in the inner or outer ring is determined by how many edges it has. The inner ring is made of texts with several connections to other texts that make up their own little clusters, mostly fragments of the Talmud or biblical verses. On the other hand, the texts in the outer ring tend to have only two or three edges. They’re just packed more closely together, which is what leads to the thicker looking band of color. Whether a cluster ends up in the inner sphere or in one of these outer rings is determined by both the number of edges and whether one of those edges connects to the massive nodes in the center. And, finally, we reach the September graph. Again, same basic principle, but the contents are slowly shifting inwards. Nodes that, in previous graphs, appeared in the outer circles have now gained access to the inner sphere (where they have been indoctrinated in the secret mysteries of graphs practices, no doubt), while the outer ring includes commentaries on and translations of biblical verses (which tend to only have one edge, to the verse they are translating).

This suggests that, as new nodes are added, earlier nodes with fewer edges make more connections and gravitate towards the center of the graph while some of the newer, more obscure nodes, take their place on the outskirts. Alternatively, the nodes themselves retain the same number of connections, but the nodes to which they are connected gain more edges and they are drawn to the center by that connection. One very popular biblical verse can draw in an extraordinary number of commentaries, each of which only connect back to that verse. There is a point at which this will have to stop; the nodes currently in the outer circle are quite unlikely to build sudden networks of communications, given that the Aramaic translations, for example, are rarely referenced within the literature.

I’m not surprised to discover that Sefaria’s database was built up in this fashion; the earlier iterations were more interested in setting up the database with texts that readers would likely want to reference. Pseudo-Jonathan was probably not a priority. If the previous question drew our attention towards the anomalously overconnected, this question turns our gaze towards the obscure. Texts like the Aramaic translations catch my eye because they are precisely the kinds of texts I would expect to find in the far outer reaches of the graph. What else is out there? Are there any similarities between the texts with few connections? And what kinds of similarities might we look for?

The appeal of big data (for a given value of big) is that it promises us the possibility of looking at the ordinary en masse rather than extraordinary exemplars. The problem is deciding what to do with the ordinary now that we’ve found it.

I considered looking at only the nodes with 20 edge or fewer, but that turns out to be 99.2% of the nodes. My next step was to reevalute my definition of “a few edges” and I went back to the graph with what I thought was a more reasonable number. Going down to 4 edges brought me all the way to 92% of the nodes visible, but less than 20% of the edges. And, finally, 75% of the nodes on the graph only have one edge. That’s about 228,000 nodes out of 305,000. As a note, fewer than 5% of the edges are part of this graph, which means that 25% of the nodes are responsible for 95% of the edges.

My original goal, for those who remember, was to look at these nodes with few connections and see what they have to say for themselves. This is where Gephi becomes less useful and so I ended up back in Excel, messing around with spreadsheets. (Yes, I know, there are better methods. But I know how to use excel!)

A log graph of the number of nodes with one degree in each category

Percentage-wise, most of the commentaries are dead ends. Which is to say, they have one edge and that edge connects to the biblical verse upon which they are commenting. Interestingly enough, the same is true for the Halakhic writings. 70% of those texts only have one edge. Which suggests a chain of transmission that only references the conclusions of a previous work and that only has its conclusions referenced by the next in the chain. And given that we are working with with individual verses in the Bible and sections of sections in Halakhic literature, it’s not surprising to find such divisions.

On the other hand, only 3% of the Mishnaic nodes have just one edge. Given the rest of the graph, that is rather an anomaly.[4] My first impulse was to blame the Babylonian Talmud (as one does) except that only accounts for 37 of the 60 tractates. Then I remembered that the absence of the Jerusalem Talmud from my scholarly interests does not disqualify it from having an effect on the graph. And yet both the Babylonian and the Jerusalem Talmud leave out the entire order “Taharot” with the exception of one Tractate. If one sixth of the Mishnah remains uncommented, shouldn’t the number of nodes with only one edge be higher?

Apparently not. I was still inclined to blame the Gemara’s selective nature for this particular graph, but it pays to be thorough. I checked the list of the Mishanyot with only one edge and, while the majority of them were from the minor tractates not discussed in the Babylonian Talmud, there were still random sections of the Mishnayot even in the more well known tractates, such as those dealing with marriage, damages, ethics. So there’s another question for another researcher. What is so strangely uninteresting about these sections of the Mishnah?

Finally, I want to return to Tanach as I found those results to be anomalous in a different direction. I found it hard to believe that 37% of the verses in Tanach have only one edge, which is to say that only one commentator or translator has taken the time to interpret them.

My instinct was right in this case. Sefaria counts translations as part of “Tanach” rather than as commentaries, despite the translators Onkelos and Jonathan taking full advantage of translation as a form of interpretation. So when I checked how many of the Tanach nodes were actually translations, I found that the number was roughly 33%. Conveniently, when I excluded the translations, I found only 4% of Tanach nodes had one edge. So the math works out nicely and there’s our 37%.

Some off the cuff Sefaria testing also suggests that some of these numbers are not accurate. There are some verses listed in my 1 degree column that clearly have more than one connection on the site itself. But that may be an artifact of the work that continues to be done on the site. Current stats from the site say that they’re up to 400,000 edges.
Perhaps it’s time for another multicolored Eye of Sauron.

As my readers have undoubtedly noticed, these forays into visualization are not focused on specific research agendas. I’m using this space to figure out what it means to work with visualizations in the humanities, what kind of work they can do and what we can ask of them. I don’t want answers, I simply want new and interesting questions. And I want to work with the texts on a visceral level, something that makes creating these visualizations surprisingly rewarding. I know that all the work we do is about creating knowledge, but it feels more visceral when I can watch the shape and size of the textual representations change on the screen. Taking ownership and making knowledge in full technicolor is what we’re all about here.

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Alternatively, one could ask whether we could build a program that looks for biblical allusions. Especially within the 24 books of the Bible, the language is constrained enough that we might be able to manage it. It would be interesting to note, first of all, how many of those linguistic connections were already set out in the Talmud and Midrash using the rules of “hekeish” or the recurrence of root words. And to compare those with the ones generated by a machine.  ↩


The problem of art is a tricky one, because we don’t see ourselves as in the business of creating beauty. We study it and, if we can, we create it in addition to our critical analysis. But that’s not quite the same thing.  ↩


“Hey! Listen!”  ↩


For the purposes of this conversation, I’m leaving out the categories “Other”, “Response” and “Dictionary” as they make up a total of 6 nodes out of 305,000.  ↩

Sefaria II: The Map, the Territory and the Sukkah

My first post in this series dealt with the possibilities of Sefaria and what mapping such a system would look like at all. This, my second post, will jump to the opposite end of the spectrum. What are the limits of this kind of work and, perhaps more crucially, how do we make those limits work for us?

But first, a status update:

As many of you probably already noticed, the previous post in this series was featured in Wired’s science blog. You can find it here: The Network Structure of Jewish Texts. I was thrilled to have the work featured and I am so glad to see The Sefaria Project getting this kind of recognition.

Speaking of the project, a recent update to the database has increased the number of links from ~87,000 to over 150,000. This is incredibly exciting (obviously!) because it not only marks Sefaria’s continued growth, but also means that I have more data. So future posts in this series will draw on that new dataset as well and I’m looking forward to some comparative visualizations as well.

But enough about the future. Let us return to the past and the other visualizations I created with the first data set.

After negotiating with the 100,000+ nodes, I decided that I wanted something on a slightly more humanly sensible scale. I took the dataset I used for the previous visualizations and combined the nodes so that each node no longer represented a verse or a small section, but an entire book. This meant I only had ~400 nodes, a far more legible graph (at least by my standards).

So this is the map, arranged in a circle according to the category of text. The size of the node corresponds to the degree (how many connections it has) while the color corresponds to the kind of node. Edge weight or line thickness corresponds to how many connections exist between each node. The thicker the edge, the more references between the source node and the target.

Here is the key to the map:

• Blue: Biblical texts
• Green: The Talmud
• Red: Mussar
• Indigo: Mishnah
• Yellow: Midrash
• Green: Philosophy
• Magenta: Halacha
• Purple: Commentaries and Exegeses

This image tells a very different story than the map in the last post.  That map was a big data artifact (for a given value of big); it worked on the micro level to create macro sized connections. This graph is human scaled, which makes it more interesting to interpret, but perhaps less interesting to discuss observations about.

The strongest connections (by which I mean the thickest edges) are between the individual books of the Talmud and Rashi’s commentary on that book. Almost as thick are the connections between the five books of the Torah and their commentaries. This is not surprising. Rashi is the exegetical commentator for the Talmud; his commentary appears on the inside of every page and, as Haym Soloveitchik points out in his essay on the printed of the Talmud page, Rashi democratized the Talmud. Rashi is an indispensable learning aid, which also explains why Sefaria might make it a high priority to have all those links in place. This tracing of explicit references is the area in which Sefaria excels. Of course, there are other kinds of connections.

The Bible, specifically the five books of the Torah, are an interesting case study in what the current database can and cannot display. The most interesting piece of information, at least to me, is the paucity of connections between the Biblical books themselves. My immediate reaction was “Of course there are so few links!” After all, the network of reference and commentary relies on the presence of texts further along the timeline that can speak of the earlier texts. And the Bible does not make a practice of citing its own chapter and verse (especially because the chapters as we know them were introduced over 1,000 years after the closing of the canon). Figure 2 gives a better sense of what I’m talking about.

Figure 2

Here, you can see all the books of the Bible in the inner circle and, while there are some connections between the individual books (most notably the 5 books of the Torah to texts in Prophets and Writings), those edges seem scarce compared to the suffusion of green that encroaches from the Talmud’s corner and that signifies the interconnectedness of the Talmudic tractates.

Yet assuming that the Bible is not self-referential would be another kind of mistake. Many of the prophets speak about the covenant between God and Abraham, the exodus from Egypt, the calamities that might befall a recalcitrant king as they did that king’s father. And those are just the obvious, semantic references. The poetry of the prophets, the psalms and the language of the 5 megillot are just some examples of texts that use literary allusion and similarities of language to reference one another. So the network of references within the biblical texts are present, but they are not really the kind of references that Sefaria is set up to import wholesale. This is where the crowd-sourced nature of Sefaria really has a chance to shine; in a few years, it can become a repository of all the different possible connections between texts – an archive of what people think they see and how readers work with the texts. Sefaria has this capability built in – there is an option to add “allusion”s between one text and another, but those have to be added manually and individually. So check back in a few years.

This leads towards the point I allude to in my title. The graph is not really a record of Jewish texts as such, but a record of these texts as they are integrated into Sefaria. To borrow a well-known quote from Alfred Korzybski, “the map is not the territory”. Bearing this useful adage in mind, we can turn to what was my biggest question when looking at this graph. What is going on with Sukkah?

Sukkah is one of the 37 tractates of the Gemara*. It is neither the longest nor the shortest, not the most complex to grasp, nor the simplest. Based purely on my knowledge of the Talmud, I can’t think of a single reason why Sukkah should be far and away the largest of the tractates present.

And yet there it is. There are two possible kinds of answers. The first is that there is something special about Sukkah that sets it apart from the other tracates. Maybe there is something that I am not aware of or maybe this is a fascinating new discovery about the tractate itself. The second possibility is that something happened during the creation of this dataset to give Sukkah significantly more edges as compared to the other tractates.

The practical distinction between these two answers is that the former assumes that Sukkah is an actual outlier that is referenced significantly more often than the other tractates. The latter assumes that Sukkah is actually representative of what all the tractates should look like and the extra edges that it possesses represent data that has only been entered for Sukkah, but should eventually be added for the rest. (The third possibility is a data error. I’m discounting that because I looked back at the actual data and, as I’ll get to in a minute, it’s pretty clear that it’s not an error. But it is always wise to assume human error first.)

So which is it? How does one pinpoint which of the possibilities is more likely? Well, this is how I did it.

I created an ego graph of tractate Sukkah. The ego graph is a graph that shows only the nodes that connect to a specific node. So this graph shows all the nodes that connect, one way or another, to Sukkah.

Figure 3

The giant green blob in the hat is Sukkah. The collection on the left are all the biblical, Talmudic and halachic sources that refer to or are referenced in Sukkah. But what’s interesting is the cloud of small nodes surrounding Sukkah on the right. Those nodes are almost entirely from Maimonides’ Mishneh Torah, one of the foremost works of halachic literature and, more crucially for our purposes, a text that references pretty much every tractate of Talmud. There should be edges between the Mishneh Torah and each and every green node here. The absence of those edges suggests that it is the dataset that is incomplete and that Sukkah, rather than an outlier, is the node that most closely represents the textual connections that exist.

So that’s cool. By looking at the node as an extraordinary case, we uncover evidence of its ordinariness. That leaves us with an entire different set of questions. What happened to Sukkah? Why did someone take the time to add all these edges to Sukkah?

I can think of several possibilities.

1. Daf Yomi. Daf Yomi is the practice of learning one folio (front and back) of Gemara a day and, in 7 1/2 short years, completing the entire Talmud. About 6 months ago, Daf Yomi covered tractate Sukkah. It’s possible that some Daf Yomi scholar discovered Sefaria right when he (statistically speaking, Daf Yomi scholars are he) started Sukkah and decided that, as part of his daily study, he would add the connections between the Talmud and the Mishneh Torah. This doesn’t explain why he stopped after Sukkah – there have been four tractates since Sukkah  – but it’s a start.
2. Pedagogy. An educator decided to introduce the concept of the halachic chain of tradition  using digital tools and assigned their students to collaboratively edit Sukkah by adding the connections between the section they were learning and the halachic literature. So, as part of a classroom module, these students entered this data. This seems like a lot of data for students to enter manually, but it is certainly a possibility.
3. It was a test of an automatic importing system. The powers that be were testing to see whether they could import the edges between the Talmudic texts and their halachic commentaries . Sukkah just happened to be the one they tested.

There are probably more possibilities, but I think that covers the basic kinds of users – the scholar, the educator, the technologist. Each of whom could be responsible for this anomaly. (By the way, if any of my readers have inside knowledge and knows what actually happens, I would appreciate anything you have to say.) When looking at a dataset like this, I find that my inclination is to start asking about the data. What would it mean to ask instead about the users and the development of the dataset? Or, to indulge in both my impulses, how can we study the data and the dataset in tandem? How do we mediate between the impulse to assign meaning to the data and the equally compelling impulse to assign it to the dataset? What exactly should I be studying?

And that is the question with which I leave you with and to which I invite your responses. What intrigues you about these visualizations? What would you like to talk about? In the crowd-sourcing spirit of Sefaria, I would like to augment my questions with yours. What would you like to know?

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*Brief technical note – the Mishnah and the Gemara together make up the Talmud. However, both the term “Talmud” and “Gemara” are colloquially used to refer to the tractates that include the Mishnaic text and the Gemara that accompanies it.

Sefaria in Gephi: Seeing Links in Jewish Literature

How do you visualize 87,000 links between Jewish texts?

The answer, at least when one is working on an ordinary iMac, is very slowly.

The better–by which I mean more accurate and productive–question is: How do you meaningfully visualize the relationships between over 100,000 individual sections of Jewish literature as encoded into Sefaria, a Living Library of Jewish Texts?

The key term for me is meaningfully – working at this scale means I have to get out of my network comfort zone and move from thinking about the individual nodes and their ego networks towards a holistic appreciation of the network as a structural entity. I’m not able to do that quite yet, at least not in this post. This is the first post in a series of explorations  – what kinds of graphs can I make with this information and what information can I get from it (or read into it)?

This project and, perforce, this series is another side of the research questions that I’m currently grappling with – how do the formal attributes of digital adaptations affect the positions we take towards texts? And how do they reorganize the way we perceive, think about and feel for/with/about texts?

Because this is Ludic Analytics, the space where my motto seems to be “graph first, ask questions later,” it seemed an ideal place to speculate about what massive visualizations can do for me.

Let’s begin with a brief overview of Sefaria. Sefaria is a comparatively new website (launched in 2013) that aims to collect all the currently out-of-copyright Jewish texts and not only provide access to them through a deceptively simple interface, but also crowd-source the translations for each text and the links between them. For example, the first verse of Genesis (which we will return to later) is quoted in the Talmud (one link for every page that quotes it), has numerous commentaries written about it (another link for every commentary), is occasionally referenced in the legal codes and so on. Here’s a screenshot of the verse in Sefaria.

Sefaria Screenshot

You can see, along the sides, all the different texts that reference this one and, of course, if you visit the website, you can click through them and follow a networked thread of commentaries like a narrative. Or like a series of TVTropes articles.

Sefaria did not invent the hyperlinked page of Rabbinic text. Printed versions of the Bible and the Babylonian Talmud and just about every other text here–dating all the way back to the early incunabula–use certain print conventions to indicate links between texts and commentaries, quotations and their sources. The Talmud developed the most intricate page by far, but the use of printing conventions such as font, layout and formal organization to show the reader which texts are connected to which and how is visible in just about every text here.

What Sefaria does (along with any number of other intriguing things that are not the topic of this post) is turns print links into hyperlinks and provides a webpage (rather than a print page) that showcases the interconnectedness of the literature. Each webpage is a map of every other text in Sefaria that connects to the section in question, provided that someone got around to including that connection. Thus we see both the beauty and the peril of crowdsourcing.

So the 87,000 links to over 100,000 nodes that I was given (thank you @SefariaProject!) are not exactly a reflection of over 2,000 years of Jewish literature as such, but a reflection of how far Sefaria has come in crowdsourcing a giant digital database of those 2,000 years and how they relate to one another. That caveat is important and it constrains any giant, sweeping conclusions about this corpus (not that I, as a responsible investigator, should be making giant sweeping conclusions after spending all of two weeks Gephi-wrangling). Having said that, the visualizations are not only a reflection of Sefaria’s growth, but also a way to reflect on the process of building this kind of crowd-sourced knowledge.

But before subsequent posts that analyze and reflect and question can be written, this post in all its multicolored glory must be completed.

To return to my very first question,  how do you visualize 87,000 links?

Like this:

Figure 1

 

 

This is Sefaria. Or a cell under a microscope. It’s hard to tell. Here’s the real information you need. This graph was made using the Gephi plugin for OpenOrd graphing, a force directed layout optimized for large datasets.* The colors signify the type of text. Here’s the breakdown.

Blue – Biblical texts and commentaries on them (with the exception of Rashi). Each node is a verse or the commentary by one author on that verse.

Green – Rashi’s commentaries. Each node is a single comment on a section

Pink – The Gemara. Each node is a single section of a page.

(Note – these first 3 make up 87% of the nodes in this graph. Rashi actually has the highest number of nodes, but none of them have very many connections)

Red – Codes of Law. Each node is a single sub-section.

Purple – The Mishnah. Each node is a single Mishnah.

Orange – Other (Mysticism, Mussar, etc.)

The graph, at least as far as we can see in this image, is made up almost entirely of blue and pink nodes and edges. So the majority of connections that Sefaria has recorded occur between Biblical verses and the commentaries, the Gemara and Biblical references and the Gemara referencing itself.

Size corresponds to degree – the more connections a single node has, the larger it is. The largest blue node is the first verse of Genesis.

On the one hand, there is an incredible amount of information embedded in this graph. On the other hand, it’s almost impossible to read. There are some interesting things going on with the patterns of blue nodes clustering around pink nodes (the biblical quotations and their commentaries circling around the pages of the Gemara that reference them, perhaps?), but there are so many nodes that it’s hard to tell.

There’s also a ton of information not encoded into the graph. Proximity is the biggest one. There is absolutely nothing linking the first and second verses of Genesis, for example. Arguably, linear texts should connect sequentially and yet the data set I used does not encode that information. So this data set conveys exclusively links across books without acknowledging the order of sections within a given book.

But, as I told my students this quarter, the purpose of a model is not to convey all the information encoded in the original, but to convey a subset that makes the original easier to manage. This model, then, is not a model of proximity, It is purely a model of reference. Let’s see what happens when we look at it another way.

Figure 2

Gephi does not come with a spatial layout function, but there are user-created plugins to do this kind of work. This is the same dataset as above, except arranged on a Cartesian plane with the X axis corresponding to In Degree (how many nodes have that node as a target for their interactions) and the Y axis corresponding to Out Degree (how many nodes have that node as a source for their interactions).** The size corresponds to a node’s Betweenness Centrality – if I were to try and reach several different nodes by traveling along the edges, the bigger nodes are the nodes I am more likely to pass through to get from one node to another.

The outlier, obviously, is Genesis 1:1. It has far and away the most connections and, especially based on its height, is the source for the greatest number of interactions. (That probably means that, out of all the information Sefaria has collected so far, the first verse of Genesis has the most commentaries written about it). It’s not the most quoted verse in Sefaria, that distinction belongs to Exodus 12:2 (the commandment to sanctify the new moon, for those who are wondering). Second place goes to Deuteronomy 24:1 (the laws of divorce) and third goes to Leviticus 23:40 (the law of waving palm branches on Succot).*** So for this data set, most quoted probably signifies most often quoted in the legal codes in order to explicate matters of law. And while the commentaries tend to focus on some verses more than others, the codes seem to rely almost exclusively on a specific subset of verses that are related to the practices of mitzvoth. I think I was aware of this beforehand, but the starkness of the difference between Genesis 1:1 and Exodus 12:2 is still surprising and striking.

Working with Betweenness Centrality as a measure of size was interesting because it pointed towards these bridge texts – statistically speaking, Genesis 1:1 is the Kevin Bacon of Sefaria. You are more likely to be within 6 degrees of it than anything else.

There are a few other interesting observations I can make from this graph. The first is that the Gemara is ranged primarily along the Y axis, suggesting that the pages of the Gemara are more rarely the target for interactions (which is to say that they are not often quoted elsewhere in Sefaria) ,but more often the sources and, as such, quote other texts often and have substantial commentaries written about them. Because one of the texts quoted on a page of Gemara is often another page of Gemara, you do see pages along the X axis, but none range as far along the X axis as along the Y. While there are texts that are often the target of interactions, the Gemara is, overall, the source.

This is in contrast to the Biblical sections, which occupy the further portions of the X axis (and all the outliers are verses from the five books of the Torah). So the graph, overall, seems to be shading from pink to blue.

Which brings me to another limitation in my approach. Up until now, I have been thinking about these texts as they exist in groups, using that as a substitute for the individual nodes that would ordinarily be the topic of conversation. So what happens when I create a version of the graph that uses color to convey a different kind of meaning and no longer distinguishes between types of texts?

Figure 3

Sefaria, taste the rainbow.

In this graph, color no longer signifies the kind of text, but the text’s degree centrality. The closer to the purple end of the rainbow, the higher number of connection the node has. Unsurprisingly, Genesis 1:1 is the only purple node.

It’s interesting to note that the highly connected nodes on the right of the graph are all connected to a large number of lower level nodes. There are no connections between the greens and yellows near the top of the page and the blues down on the right. Why is there such a distinction between nodes that reference and nodes that are referenced? Why is the upper right quadrant so entirely empty? Does this say something about the organization of the texts or about the kinds of information that the crowd at large has gotten around to encoding? Or is it actually a reflection of the corpus – texts that cite often are not cited in turn unless they are in the first book of the Torah?

If you have any questions, thoughts, explanations, ideas for further research with this data set or these tools, suggestions for getting the most out of Gephi, please leave your comments below.

Coming soon (more or less): What happens when we look at connections on the scale of entire books rather than individual verses?

Bonus Graph: A Circular graph with Genesis 1:1 as the sun in what looks like a heliocentric solar system. Why? Well, it seemed appropriate.

One note on this graph. You can see the tiny rim of green all around the right edge – those are the tiny nodes that represent Rashi’s commentaries and make up more than 1/3 of all the nodes in the graph. The inner rings, at least what we can see of them, tend towards Biblical verses and their commentaries. The Gemara is almost all on the outside. Of course, those distances are artifacts of deliberately placing Genesis 1:1 at the center, but they are interesting nonetheless.

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*Force directed, to provide a very brief summary, means that the graph is designed to create clusters by keeping all the edges as close to the same length as possible. Usually it works by treating edges as attractive forces that pull nodes together and the nodes themselves as electrically charged particles that repulse one another.

**At least in this data set, the source is the text under discussion, so if one were to look at the connection between Genesis 1:1 and Rashi’s commentary on Genesis 1:1, the Biblical verse is the source and the commentary the target. Conversely, if one were looking at a quotation from Genesis in a page of the Gemara, the page of Gemara would be the source and the verse in Genesis the target.

***Based on further explorations of the data set according to less fine-grained divisions, I am convinced that anything having to do with the holiday of Succot is an outlier in this dataset. More on that in another post.

Revisiting the Social Networks of Daniel Deronda

My twitterstream overflowed, in the past few days, with tweets about the uses, misuses and limits of social networking.* Coincidentally (or perhaps not, given the identity of at least one retweeter), we discussed the role of social network graphs in humanistic inquiry in this week’s session of Alan Liu’s “Intro to Digital Humanities” class. For those of you following along, we are #engl236 on Twitter and, last week, we made graphs. So I am going to interrupt my glacial progress through the possible uses of R**and put the longer-form meditation on what I am trying to do with these experiments in statistical programming on hold in order to talk about my latest adventures in social network graphing.

As longtime readers of this blog will remember, this is not my first foray into Social Network graphing. Nor is it my second. This gave me a huge advantage over many of my colleagues (sorry!) because I had already spent hours collecting and formatting the data necessary to graph these kinds of social networks. Since I wasn’t going to map new content, I thought I would at least learn a new program to handle the data. So I returned to Gephi, the network visualization tool that I had failed to master 18 months ago.

And promptly failed again.

PSA: If you have Apple’s latest OS installed, Gephi will not work on your machine. I and two of my classmates discovered this the hard way. Fortunately, the computers in the Transcriptions Lab are–like most institutional machines–about an OS and a half behind and so I resigned myself to only doing my work on my work computer.  After some trial and error, I figured out how I needed to format the csv file with all my Daniel Deronda data and imported it into Gephi. After some more trial, more error, and going back to the quickstart tutorial, I actually produced a graph I liked.

In this graph, size signifies “betweenness centrality” which is a marker of how important a circle is in the graph according to how many connections the node has and how often that node is necessary for getting places in the network (i. e., how often the shortest path between two other nodes is through this node), which means that the node’s size indicates how vital that person is to other people’s connections as well as how many connections they themselves have. Color signifies grouping. Nodes that are the same color are nodes that have been grouped together by Gephi’s modularity algorithm…which is Gephi’s function for dividing graphs into groups.

So here we see three groups, which can be very roughly divided into Gwendolen’s social circle, Deronda’s social circle and Mirah’s social circle. There’s something delightful about the fact that the red group is made up entirely of the members of the Meyrick family and the girl they took in (Mirah). So Mirah truly becomes a member of the Meyrick family.

As this is a comparative exercise, I’m less interested in close-reading this graph and more interested in thinking through how it compares to yEd.

Gephi is certainly more aesthetically pleasing than yEd, especially given the settings I was using on the latter. And, unlike yEd, Gephi can very easily translate multiple copies of the same interaction into more heavily weighted lines, which helps provide a better idea of who speaks to whom how often in the novel (something I had been struggling with last year). At the same time, yEd’s layout algorithms seem far more interesting to me than Gephi’s “play around with Force Atlas until it looks right” approach. So while the layout does, I think, do a decent job of capturing centrality and periphery, it is less interestingly suggestive than yEd.

The other failing that Gephi has is the lack of an undo button. This might seem trivial to some of you, but being able to click on a node, delete it from the graph and then quickly undo the deletion was what made it so easy for me to do “Daniel Deronda without Daniel (and, erm, Gwendolen)”. With Gephi, I have this paranoid fear that I will lose the data forever and it will automatically save and I’ll have to do all this work over again. After a while, I finally screwed my courage to the sticking place and deleted our main characters to produce the following three graphs.

Daniel Deronda without Daniel

Daniel Deronda without Gwendolen

Daniel Deronda without Daniel or Gwendolen

The results are interesting, although perhaps less interesting than the disk-shaped diagrams from yEd that demonstrated changes in grouping. yEd allowed for some rather fine-grained analysis about who was regrouped with whom. On the other hand, Gephi makes it clear that both Gwendolen and Deronda tie together groups that, otherwise, are more distinct, as shown by the sudden proliferation of color in the first and third graphs particularly. Gephi makes it easy to see Deronda’s importance in tying many of the characters together. His influence on the networks is far stronger than Gwendolen’s.

Now, for the sake of comparison, here are the Gephi and yEd graphs side by side.

I have not yet performed a more complete observational comparison of the layout, centrality measures and grouping algorithms in Gephi versus yEd (which, I admit, would begin with researching what they all mean) and the relationship between how data is presented and what questions the viewer can ask, but here are my preliminary reactions. Gephi does a far better job of pointing to Deronda’s importance within the text while yEd is better at portraying the upper-class social network in which Gwendolen in enmeshed. And while Gephi’s layout invites the viewer to think of its nodes in terms of centrality and periphery, yEd’s circular layout structures one’s thought along the lines of smaller groups within networks. Different avenues of inquiry appear based on which graph I look at.

This comparison produces three different questions.

1. How do you know when to use which program? Can one tell at the outset whether the data will be more interesting and approachable in Gephi, e.g., or is this the perfect application of the “guess and check” approach where you always run them both and then decide which graph is more useful for the kinds of questions you want to ask. Are my conclusions here, about Gephi’s focus on centrality versus yEd’s focus on group dynamics, representative?
2. How meaningful are the visual relationships one perceives in the network?
   1. Let’s take the graph above as an example and go for the low-hanging fruit. Young Henleigh, the illegitimate son of Grandcourt is way down at the bottom of the graph, connected unidirectionally to his father (his father speaks to him, but he does not speak back) and bidirectionally to his mother, with whom he converses. Gephi has colored him blue, indicating that, at least according to Gephi’s grouping algorithm, he is more closely associated with the other blue characters (a group made up predominantly of those who show up in Daniel’s side of the story and who I am valiantly resisting calling the Blue Man Group). Arguably, this is because those in Deronda’s circle talk slightly more about the boy since they have heard rumors of his existence, while those in Grandcourt’s social circle have not. And Henleigh’s repulsion distance is another indicator of how Grandcourt ignores his son and keeps his family at a distance.
   2. That is, I think, a fair reading of the book Daniel Deronda. My conclusions are borne out in the text itself and are justifiable within the larger narratives of Grandcourt’s treatment of others, a topic that I’ve written about several times over the course of my graduate career. But is it a fair reading of the graph? Am I taking accidents of layout as purposeful signals? Or are my claims, grounded as they are in edge distance and modularity, reasonable?
3. In addition, did the graph actually tell me this information in a way that the book did not or did it simply remind me to look at what I already knew? This is part of an old and still unanswered question of mine – will the viewing of the social network graph ever really be useful or is it the decisions and critical moves that go into making the graph that produce results?

Obviously, this last question only applies to work like mine, where the graph is hand-coded and viewed as a model of an individual text. In cases where this work is mostly automated and several hundreds of novels are being studied for larger patterns of interactions, the question of whether the graph or the making thereof produces the information is irrelevant.

But the question of what kinds of meaning can be located in layout and pattern is still crucial, especially when one is comparing how different networks “look”. This may be a particularly pernicious problem in literary criticism and media studies: we’re trained to look at texts and images and treat them as…intentional. Words have meaning, pictures have meaning and we talk about this larger category of “media objects” in a way that assumes that their constituent parts have interpretable significance. This is not the same as claiming authorial intentionality, it’s simply an observation that, when we encounter a text, we take it as given that we can make meaning using any element of that text that impinges on our consciousness. There are no limits regarding what we can read into word choices, provided we can defend our readings and make sense out of them. Is that true of graphs? Are we entitled to make similar claims by reading interpretations into features of the layout and with the only test of said interpretation’s veracity our rhetorical ability to convince someone else to buy it? For example, could I claim that Juliet Fenn’s position on the graph between Deronda and Gwendolen shows that she, and all that she stands for, comes between them?  My instinct is to say no. But the same argument about place applied to a different character makes perfect sense. Mordecai’s place is between Deronda and the group of Jewish philosophers on the far right is emblematic of how he connects Deronda to his nation and how he is the one who rouses Deronda’s interest in Zionism.

I can think of three off-the-cuff responses to this problem. The first is to say that location is a fluke and, when it corresponds to meaning, that’s an accident. This feels unsatisfying. The second is to say that there is something about Juliet Fenn that I’m missing and, were I to apply myself to the task, I could divine the reason behind her placement. This is differently unsatisfying, not because I don’t think I can come up with a reason, but because I am afraid that I can.*** And if I succeed in making a convincing argument, is that because I unearthed something new about the book or because I’m a human being who is neurologically wired to find patterns, a tendency exacerbated by my undergraduate and graduate training in the art of rhetorical argument? In short, the position that all claims that “can” be made can be taken seriously is only marginally less absurd than the claim that all layout elements are always meaningless and, consequently, any meaning we make or find is insignificant. The third response heads off in a different direction. Perhaps my discomfort with reading these networks lies not in the network, but in my own lack of knowledge. I have not been trained in network interpretation and I need to stop thinking like a literary theorist and start thinking like a social scientist. I need to learn a new mode of reading. This, while perhaps true, also leaves me dissatisfied. I am not, fundamentally, a social scientist. I am not looking for answers, I’m looking for interesting questions/interpretive moves/ideas worth pursuing. While it would be very cool to show, in graph form, how Mordecai’s ideology spreads to Daniel and how ideas act as a kind of positive contagion in this novel, that theory is not stymied if there is insufficient data to prove it. I can take imaginative leaps that social scientists responsible for policy decisions must absolutely eschew.

Which means it is time to think about a fourth position. If we, as scholars of media in particular, are going to continue doing such work, then we need a set of protocols for understanding these visualizations in a manner that both embraces the creativity and speculative nature of our field while articulating the ways in which this model of the text corresponds to the actual text. Such a set of guidelines would  be useful not only as a as a series of trail markers for those of us, like me, who are still new to this practice and unsure of where we can step, but also as a touchstone that we can use to justify (mis)using these graphs. If the sole framework currently in existence is one that does not account for our needs, we may find ourselves accused of “doing it wrong” and, without an articulated, alternative set of guidelines, it becomes exponentially more difficult to respond. On the most basic level, this means having resources like Ted Underwood’s explanation of why humanists might not want to follow the same steps that computer scientists do when using LSA available for network analysis. Underwood explains how the literary historian’s goal differs from the computer scientist’s and how that difference affects one’s use of the tool. Is there a similar post for networks? Is there an explanation of how networks within media differ from networks outside of media and advice on how to shift our analytic practice accordingly? Do we even have a basic set of rules or best practices for this act of visualizing? And, if not, can we even claim these tools as part of our discipline without actually sitting down and remaking them in our image?

I don’t want to spend the rest of my scholarly career just borrowing someone else’s tools. I want Gephi and yEd…and MALLET and Scalar and, yes, even R to feel like they belong to us. Because right now, for all that I’ve gotten Gephi to do what I want and even succeeded in building a dynamic graph of the social network of William Faulkner’s Light in August (which told me nothing I did not already know from reading the book), I still feel like I’m playing in someone else’s sandbox.

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*Granted, this is Twitter and so three posts, each retweeted several times, can make quite a little waterfall.

**I will say that the R learning curve made figuring out Gephi seem nearly painless by comparison.

***In the interest of proving a point, a short discussion of Juliet Fenn: Juliet Fenn’s location between Deronda and Gwendolen and at the center of the graph is significant precisely because she is the character who represents what each of them is not. Juliet is of the more aristocratic circle defined by Sir Hugo and his peers and, unlike Daniel, actually belongs there by birth. She beats Gwendolen in the archery contest, which proves her authenticity both in terms of talent and, again, aristocracy. Were either Daniel OR Gwendolen authentically what they present themselves as (and, coincidentally, who their co-main-character perceives them to be), Juliet Fenn would be Gwendolen’s mirror and Deronda’s ideal mate. As neither Gwendolen nor Daniel are, in fact, who they seem to be, Juliet is neither. She is merely a short blip during the early chapters of the book who can be easily ignored until her graphic location discloses the subtle purpose of her character–the idea of a “real” who Gwendolen cannot be and Deronda cannot have. Of course, neither character explicitly wants or wants to be Juliet. This isn’t meant to be explicit, merely to color our understanding of the otherness of Deronda and Gwendolen. It’s not that Juliet Fenn keeps them apart per se, but the discrepancies between who she is and who they are, as illustrated by the graph, is what makes any relationship between Gwendolen and Deronda impossible.

Bar Graphs and Human Selectiveness

Two weeks worth of struggling with R and putting in my own texts (feel free to guess which one I used) has left me feeling less accomplished than I would have liked, but less filled with encroaching terror as well. I am capable of following instructions and getting results, so while the art of doing new things (and really understanding the R help files) is still beyond me, I think I have enough material to start talking about Daniel Deronda again.

Daniel Deronda is a text that seems split into two halves. One of the things I discover when I reread this book is that there are many more chapters than I remember with both Deronda and Gwendolen “on screen together”. So are these two separate stories or are they two utterly intertwined texts?

In order to test how separate the two storylines are, I looked at the word frequencies of both “Deronda” and “Gwendolen” in each chapter to see whether they were correlated. So, in this case, a positive value means that Deronda showing up in a chapter increases the likelihood of Gwendolen showing up while a negative correlation means the opposite.

The correlation between Deronda and Gwendolen is -0.465. (As a reminder, correlations run from 1 to -1). So that’s actually pretty high, given that book chapters are complex objects and I know that they interact a fair amount over the course of the book. But there’s actually a better way to test for significance. We can look at the likelihood of this correlation having occurred by random. Again, drawing on Text Analysis with R, by Matthew Jockers, I had R rearrange the appearance 10,000 times and then generate a plot of what the correlations were. Unsurprisingly, it looks like a normal curve:

So if the frequency of each name per chapter was distributed randomly, you would be statistically likely to see little correlation between them. For those interested in some more specific numbers, the mean is -0.001858045 and the standard deviation is 0.1200705, which puts our results over 3 standard deviations away from the mean. That little blue arrow is -0.465.

All that says, of course, is that it’s highly unlikely that these results occurred by chance and that they are, in some sense, significant.* Which, to be fair, no kidding. My initial, subjective reading told me they were negatively correlated as well. And there has to be a better reason to do this kind of work than just to prove one’s subjective reading was right.

Which is where our next graph comes in. Now that I know that the two are negatively correlated, I can turn to the actual word frequency per chapter and see what the novel looks like if you study character appearance.

And, for fun, I threw in two other characters who I see as central to the plot to see how they relate.

 

I highly recommend clicking on the graph to see a larger view.

Here’s where things get interesting to the human involved. The beginning of the novel happened exactly as expected – Eliot starts the story in medias res and then goes back to first tell us Gwendolen’s history and then Deronda’s. And then the name game gets more complicated about halfway through when Mirah and Mordecai** enter the picture. By the last few chapters, there is very little Gwendolen and the story has settled firmly around Deronda, Mirah and Mordecai. All of this, again, makes sense. But it is nice to see the focus of the book plotted out in such a useful manner and it invites two kinds of questions.

The first is based on the results; going to chapters with a surprisingly high mention of a certain character, like Deronda’s last few chapters, and attempting to figure out what might be going on that causes such results. Why, after all, is Daniel the only one to venture up into the 1.2% frequency? Is there something significant about the low results around 50 and 51? What’s going on there?

The second kind of questions that this graph invites are questions about me. Why did I choose these four characters? I think of them as the four main characters in the story and yet there’s certainly a good argument to be made for at least one other character to be considered “main”.

If you’ve read the book, feel free to guess who.

Why did I leave out the frequency data for Henleigh Mallinger Grandcourt?

Honestly, I completely forgot he was important. It’s not that I don’t remember that the Earl of Grantham had an evil streak in his youth, it’s simply that I don’t think of Grandcourt as a main character in the book. That might be because one doesn’t usually think of the villain as “the main character” or it might be because I am more interested in the story of Deronda and 19th century English Jewry.

As it happens, I noticed Grandcourt’s absence because of that odd little gap in Chapter 12 where absolutely no one is mentioned. What was going on there?

I went on Project Gutenberg, checked the chapter and said “Oh. Oops.” This is the only chapter entirely (and possibly at all) from Grandcourt’s perspective, hence no mention of any other character. So why didn’t I redo the graph with Grandcourt included, given that he’s important enough to have his own chapter?

Okay, yes, sheer laziness is part of the answer, but there is another reason. Chapter 12 is the chapter in which Grandcourt announces his intention to marry Gwendolen. And notice whose name entirely fails to appear in the chapter…

This data doesn’t exactly tell us anything new – we have ample proof from Eliot that Grandcourt is one of the nastiest husbands in the British canon. But this detail invites a way of looking at people’s interactions categorized by recognizing another person by the simple act of naming them, which makes this the second time that randomly playing around with visualizations has led me towards the question interpersonal interpellation as related to empathy. 

So what do you all think? What does the graph say to you? Do you think this is a valuable way of approaching a text? And am I getting kinda hung up on this question of simply naming as a measure of empathy?

Comment below!

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* With the obvious caveat that this was a book written by a woman rather than a random letter generator so of course its results did not occur by chance, what this graph really lets us see is whether the negative correlation between the two characters allows for meaningful critical discourse. Anything under -0.5 is not really considered significant in scientific terms, primarily because it’s not useful for predictive validity, but because we’re not interested in predictive validity, we’re interested in the possibilities of storyline division, the graph validates the hunch that there’s some kind of distinction.

**SPOILER ALERT – Mordecai is actually the combined occurrence of the names Mordecai and Ezra, for reasons obvious to anyone who has read the book.

 

The Limits of Social Networks

Though we have mostly gone our separate ways over the past year, I find that I am attached to the idea of the LuAn collective and want to keep it going just a bit longer. After all, you never know when you might need a data viz blog that you co-run.

As a second year student in the English department at UCSB, I am gearing up to take (i.e. reading madly for) my qualifying exams this June. As luck would have it, I am also finishing up my course requirements this quarter, so I find myself in the…unenviable position of writing a paper on a topic that would ordinarily lie far outside my interests in the 19th century English novel: William Faulkner. So I did what any digital humanist with an unhealthy interest in visualization would do in my situation – I made a graph.

I wanted to write a final paper for this course that reflects my theoretical interests and would allow me to continue developing a subset of my digital skills. Of course, trying to get all of my interests to move in more or less the same directions is like herding kittens, but I had been seeking another opportunity to think through a novel using a social network graph and, well, I wouldn’t have to start from scratch this time. I knew how my graphing software, yEd, worked and I knew how long it took to turn a book into a collection of Excel cells denoting conversations (20% longer than you think it will take, for those of you wondering). So why not create a social network graph of one story in Yoknapatawpha?

Don’t answer that question.

Light in August is widely considered to be the most novel-like of Faulkner’s novels, which made it a good choice for my project. After all, I had experience turning a novel-like novel into a social network graph and no experience whatsoever with a text like The Sound and the Fury. Much as I was intrigued by and even enjoyed The Sound and the Fury and Absalom, Absalom!, the prospect of figuring out the rules for graphing them was…intimidating to say the least.

For all its novelistic tendencies, Light in August is still decidedly Faulknerian and, in order to work with it, I found myself either revising some of my previous rules or inventing new ones. When I worked on George Eliot’s Daniel Deronda, I had used a fairly simple set of two rules: “A bidirectional interaction occurs when one named character speaks aloud (that is, with quotation marks) to another named character. A unidirectional interaction occurs when a named character speaks aloud about another named character.”

Here are the Faulkner rules:

1. When one character speaks to another, that interaction is marked with a thicker, dark grey arrow.
2. When one character speaks about another, that interaction is marked with a thin, dark blue arrow.
3. When one character speaks to another within another character’s narration (i.e. X is telling a story and, in it, Y talks to Z), that interaction is marked with a thicker, light grey arrow
4. When one character speaks about another within another character’s narration, that interaction is marked with a thin, green arrow.

There are several changes of note here. First, I learned more about yEd and figured out how to put properties like line size and color in the spreadsheet itself so that the software would automatically map color and line weight as appropriate. This meant I could make finer and clearer distinctions than last time, at least in terms of showing kinds of communication. Second, I changed the rule about quotation marks because quotation marks don’t necessarily connote audible speech in Faulkner, nor does their absence connote internal monologue. I relied entirely on the dialogue tags in the text to decide whether a sentence was spoken aloud or not. Finally, I changed the rule about named characters. All speaking characters are represented in the graph, regardless of whether or not we are ever told their names. Had I not changed this rule, the number of characters of color represented in this graph would have fallen from 15 to 3. There are 103 distinct nodes in this graph, which means 103 characters speak in this text.

Jeffrey Stayton, in an article entitled “Southern Expressionism: Apocalyptic Hillscapes, Racial Panoramas, and Lustmord in William Faulkner’s Light in August” (which, in the interest of full-disclosure, I am still in the middle of reading), discusses how Faulkner figures racial landscapes in Light in August as a kind of Southern Expressionism. It is fitting, of course, that one of Willem de Kooning’s expressionist paintings is based on and entitled “Light in August”. But this graph highlights the relationship between fading into the background and remaining unnamed, it shows how easily racial landscapes can become racial backgrounds and how easily it is to elide the unnamed. In the Victorian novel, a certain charactorial parsimony seems to ensure that everyone who speaks is named. Daniel Deronda is 800 pages long and contains 62 character nodes. Light in August is 500 pages long and contains 103. If you remove all the unnamed characters, there are 44 character nodes. (For those of you counting, thats 38/88, close to half of the white characters, and 12/15 or four fifths of the black characters. The other 8 are groups of people, who seem to speak and are spoken to fairly often in this text.)

There are several ways to interpret this difference and I am loathe to embrace any of them without, frankly, having done more work both with Faulkner and with the Victorian novels. One of the things I find striking, though, is that Light in August seems to be making visible (though only just) things that are either not visible or entirely not-present in Daniel Deronda. Light in August is told from different characters’ viewpoints and the narration always locates itself in their perspective and confines itself to what they know. So the graph becomes a record not only of what they have seen, but also of how they have seen it.

I can hear some of you grumbling “What graph? You haven’t shown us a graph yet!”

My apologies. For that, I will give you three. Anything worth doing is worth overdoing.

1) The first graph.

Click to see it in full size.

In this graph, color corresponds to importance, as determined by number of interactions. The darker the color, the more interactions that character has had. That dark red mark in the middle is Joe Christmas.

2) The graph without the unnamed characters

Click for full size.

Colors mean the same here that it did in the previous graph.

There are several differences between the two graphs. Obviously, the second is legible in a way that the first one is not, which is not entirely a virtue. When it comes to graphing, legibility and completeness tend not to walk hand in hand. The more you leave out, the more you can see so, contra-positively  the less you can see, the less you have left out. The best-of-both-worlds solution is to use both images.

Interestingly enough, there are no unconnected nodes in the second image, even though I deleted half of the nodes in the graph. That surprised me. I expected to find at least one person who was only connected to the network through one of the unnamed characters, but there’s no such person. And many of the people who remain are not characters I would consider to be important to the story (Why has the entire history of the Bundren family remained more or less intact? Who is Halliday, anyway?)

These are questions to be solved, or at least pondered. They are, at any rate, questions worth asking. If the network remains intact without these characters, what does their presence signify? What has changed between the first graph and the second?

After all, I do have a paper to write from all of this.

I promised you a third graph, did I not? This one moves in a rather different direction. As part of its ability to organize and rearrange your graph, yEd has a grouping functionality and will divide your graph into groups based on the criteria you choose. I had it use natural clustering.

A grouping into natural clusters should fulfill the following properties:

• each node is a member of exactly one group,
• each node should have many edges to other members of its group, and
• each node should have few or even no edges to nodes of other groups.

yEd gave me 8 distinct groups, two of which had only two nodes in them.

As always, click for full-size.

I assume that when yEd said that the groups would have few or no edges to nodes in other groups, it was doing the best it could with the material I gave it. I then had yEd rearrange the positions of the nodes so that the centrality of a node’s position within a group indicates how many connections it has.

What I love about this graph is how it divides Light in August into a set of six interconnected but distinct narratives. Each group larger than two centers around a specific character or group of characters involved in one thread of narrative. Joe Christmas, who is arguably the main character, has one section (along with a plurality of the other characters of color), Lena Grove, Bryon Bunch and Joe Brown are all grouped together in another and, while they talk about the characters in Joe Christmas’s section quite often, they have only three conversations with the characters in that group. Those are the two largest groups. Percy Grimm, for all that he only appears in one chapter, manages to collect 7 other nodes around himself and does seem, in his own way, to be the protagonist of his own story who just walked into this one for one chapter and then left again. He is also the only named character in his section.

Social network graphs are, for me, a way of re-encountering a text. They strip away most of the novel and model only a small portion of what is present in the text, but that portion becomes both visible and analytically available in a new way. (I think seeing and visibility will become a theme in this paper, once I write it.) The title of this course is “Experimental Faulkner”. I like to think that this qualifies.

The Social Network (of Daniel Deronda)

Since this project’s beginning, I had toyed with the idea of doing a social network graph that would look at the relationships between all the characters in the novel. I was aware that this would be a substantially larger undertaking than any of the other visualizations I had in mind, which perhaps explains why I left it for last. Despite forewarning myself, I grossly underestimated how difficult that would be and set off to code character interactions over the course of 70 chapters in an 800 page novel. As an experience that opened up the novel to me in all sorts of new ways, it was wonderful. As a mix between skimming and data entry, it was profoundly unpleasant.

But enough lamenting the plight of the digital scholar, that’s boring. Here are the results:

Now for the specs. In order to create this graph, I needed to set some rules for what qualified as interaction. A bidirectional interaction occurs when one named character speaks aloud (that is, with quotation marks) to another named character. A unidirectional interaction occurs when a named character speaks aloud about another named character. The chart does not differentiate between two people who gossip about one another and two people who actually speak to one another. Also, the chart only shows the presence or absence of interaction, it does not add weight to the edges based on how many times interactions took place. I am aware that this is less than ideal, but as this is just my first foray into social network graphing, I have not yet worked out the full range of the software’s ability. I have the data to create that graph, just not the knowhow. But I plan to work it out when I have the chance.

Anyway, this graph was generated by the graphing software yEd. I told it to place the characters in a single circle and to use color to convey a character’s centrality (darker colored nodes have more connections to the other nodes). Then I just played around with the background because I am a sucker for light on dark presentation.

Here’s where it gets fun. I told the software to redraw the graph based on the groups it thought that the characters should be divided into (well, not in so many words, but that was how I translated the instructions in my head). The resulting graph is below.

Cool, right? The weirdest part, for me, was that Mrs. Davilow (Gwendolen’s mother) is at the center of the giant social cluster rather than Gwendolen herself. I have a few ideas as to why she might be–she’s more important than I tend to give her credit for–but I’m leery of creating post-hoc explanations for something that could simply be a software quirk. Still, it’s provocative.

The other point I want to make is about families. Here is another version of this graph, this time with immediate family members all colored the same color.

Now, it’s much easier to see which family groups are more connected throughout the novel and which are not. I find it particularly intriguing that upper-middle class families are all spread out along one giant social circle while the lower class families tend to cluster closer together as family groups.

Finally, I did one more thing with this graph. In the spirit of Franco Moretti’s work with Hamlet, where he graphed the social network of the novel and then deleted the Danish Prince from the graph, I did the same with both Gwendolen and Deronda, then told yEd to rearrange the groups based on the new data.

Okay, take a look at the two graphs.

I’d be mean and ask for your thoughts, but as I’m not sure how many of my readers have read Daniel Deronda (not to mention how many readers we have),  it would be unfair to ask you for an interpretation. Instead, I will provide you with mine. So here’s the cool thing. The families that grouped together in the previous graph but not in this one were brought together by the actions of the main character–in this case, Deronda. So Mordecai rediscovered his long-lost sister Mirah through Deronda, for example. On the other hand, the families that now group together had their lives disrupted in the book by the actions of the main characters, either Deronda or Gwendolen, depending on the family in question. So if you look at Grandcourt, pictured here with his mistress, Mrs. Glasher and illegitimate heir, Henleigh, you’ll see that he’s nowhere near them in the graph with Gwendolen. In the text, Gwendolen marries Grandcourt despite knowing that he has a mistress and son who deserve to be legitimized. (Illegitimacy is a theme in this text.) I found it absolutely fascinating that removing the characters from the graph actually mimics what removing them from the book would have done.

So here’s my invitation to you: think about how else these graphs might be able to speak. I used them to construct a specific narrative of family ties throughout the novel based on how the connections behave. How else might you produce new elements of the novel’s narrative using these kinds of graphs? And, if you’ll think back to last week’s thoughts on dynamic social network graphs, how might those really help to structure questions about the novel?

One final note–I am really pleased to have finally produced something using statistical software that I think is pretty. It makes me feel that all is not yet lost.

Back to Textual Basics

As Claire mentioned, I do have a post coming up about network graphs. And, I should note, I ended up with more static images from that experiment than from all the other experiments I performed combined. There’s something about networking that makes me want to document every single change I make to the image.

Anyway, that’s not this post. This post is a return to the problem of the pretty, which I have not touched on in a while. I am at DHSI, the Digital Humanities Summer Institute, taking a course with David Hoover on seeing what can be done with text analysis. So while the focus of the class is on playing with textual analysis and seeing what it can provide on an interpretive level (or possibly just the “hmm, I wonder what this button does” level), almost all the analyses we have done have produced, almost as an afterthought, a visualization.

So I couldn’t resist.

Here are the results of three linked analyses done in Minitab, which is statistical analytic software. My basic relationship with this software is as follows: I can interpret the results. I even have a pretty good broad idea of how the computer gets the results. But if you want a concrete explanation, I soon get very very lost in my own verbiage and I recommend that you do what I do when I don’t know what to do…turn to Google.

My emphasis here is on the visualizations, however, so let us move towards those.

1) Cluster Observations of the Daniel Deronda Chapters

So…what do we make of this?

I’ve taken a leaf out of David Hoover’s book and color coded the labels to make it easier to see patterns. The green are the chapters from Daniel’s perspective, the purple are the chapters from Gwendolen’s perspective and the blue are chapters in which the perspectives switch back and forth between them. Uncolored chapters are those from the perspective of non-main characters. This was done in Pixelmator (a reasonably-priced image editing app for Mac) at some less than salubrious hour of the night and it rather shows.

Also, I spent several minutes that felt like hours last night trying to change the colors of the dendrogram (as I discovered, the technical term for this tree is a dendrogram). Of course, when I went back to it this morning, I figured out how to do it by accident. However, given the choice between recoloring the entire bottom or simply dealing with the ugly shade of red, I chose laziness over aesthetics.

Anyway, back to the graph. So this analysis take the 990 most frequent words in the novel after all proper names and gendered pronouns have been removed* and uses them to try and determine which chapters are most like other chapters based on how they use those words. It then shows the clusters (hence cluster observations) with the heights of the linkages in the dendrograms conveying degree of similarity.

So what this tells us is that the word usage is different enough that the top 1000 words, even without names, are enough to broadly distinguish between the chapters dealing with Deronda and those dealing with Gwendolen (It’s worth remembering that this is the top 1000 of ALL words in the novel, including words like “the,” “I” and “and”). It’s not perfect, but as Eliot is writing a fair amount of free indirect discourse, I would have been more surprised if it were. I’m actually surprised that it worked as well as it did.

And if you’re wondering about that weird blue line that is separate from the entire rest of the novel, it’s a very short chapter that consists almost entirely of a letter to Deronda from his long-lost mother. I had to go and look up the chapter to see why it was weird, but once I had it made perfect sense. (The human brain, of course, can justify anything, but I happen to think mine is right in this case.)

Onto the next graph!

2) Principle Component Analysis. (Adam Crymble, this one’s for you.)

100 mfw means that these divisions are based only on the 100 most frequent words. That’s less accurate, but because I’m going to superimpose all the words used in this analysis on the graph in a minute, I need to keep the number of words down to a size that doesn’t resemble a plague of locusts.

So what’s going on here?

*crickets chirp*

Okay, here’s the version I understand. What PCA does is it takes the raw data, in this case word frequency proportions, and “rotates them through multiple dimensions” to figure out the best combination of variables that represents the variations seen by the data. Once it has worked out these components (using math far beyond my comprehension), it graphs them based on the two principle components–that is, the two that best describe the differences. And that’s the graph. (I am utterly indebted to Charles Shirley for directing me to the following link, http://www.mun.ca/biology/scarr/Lab_4_-_Adaptive_shape_variation_09Nov02.pdf, which helped me understand what is going on.)

In terms of interpretive work, Deronda and Gwendolen don’t really cluster, but they do tend to divide with Gwendolen showing up predominantly above the x axis and Deronda predominantly below it. So the second Principle Component involved in dividing these two groups of chapters is the main character. I have no idea what the first component is, though there is a way to…if not find out, to think about it.

3) Scatterplot

This is a graph that uses the same components as above, but this time it graphs the 100 words used onto the component axes so you can compare where the words fall on the above graph. Imagine it superimposed on the previous graph. (I would do so myself but I haven’t figured out a way to pull it off without making both graphs entirely illegible). It’s…an interesting way to think about the distinctions in word usage. For example, my intuitive response to the way the words are laid out is to guess that the left-right principle component tells you how much dialogue is in a given chapter. The dialogue heavy chapters are on the right. (Obviously, the principle component itself is much more mathematical than that, but in my terms it seems to equate with heaviness of dialogue). My next step would be to check the chapters themselves and see if my predictions are correct.

The fact that these graphs are appearing at the end of this portion of the project is, in its own way, unfortunate. I think of these graphs as a pedagogical tool that works best at the beginning of project. If you wish to use them as such, what I would suggest doing, once you’ve gotten up the learning curve and worked out how to read them, is use these graphs to direct future research. For example, look at the divide between Eliot’s writing when she writes dialogue heavy chapters versus when she writes as the narrator (in fact, her narrative voice is quite interesting in its own right). That might be something to look into. And coming up with explanations for why the chapters in the dendrogram divided the way they did could certainly be fun (although suspect from the perspective of valid textual explanations).

However, what I would really do is look for better ways to display the information in these charts.  Statistics programs were not invented to create beauty, unless you find order out of chaos to be beautiful. As a project for the future–perhaps over the summer–it might be worth thinking of ways to artistically reinterpret statistical charts in a manner that, though it sacrifices fidelity to the data, still conveys the information but in a manner that is aesthetically appealing and draws in the reader. Basically, I want a visualization that attracts both people who love graphs and people who start running in the opposite direction when they see one. That would be a really useful visualization.

~~~

*I removed the proper names and pronouns because I already knew the information they could give me. If I found that the best indication that a chapter was about Gwendolen was the frequency of the word Gwendolen, I might get a very accurate result, but not a very interesting one. This way, though perhaps my results will be less accurate in division, they will be more interesting.

What Are We Doing With Our Visualizations?

A colleague of mine pointed me towards the following post about Shock and Awe Graphs in the Digital Humanities. The author, Adam Crymble, makes some decidedly thought-provoking points about what graphs are meant to be doing and how data visualization can sometimes work as a tool of intimidation as well as elucidation.

So before you publish a visualization, please take a moment and step back. As in the cult classic, Office Space, ask yourself: Is this Good for the Company?

Is this Good for Scholarship?

Or am I just trying to overwhelm my reviewers and my audience?

The authors of the blog Clioviz respond to Crymble’s question with a post In Praise of Shock and Awe, which also (and unsurprisingly) has some very good points to make about the value of disseminating information via visualization. They note that a certain amount of “shock and awe” in inevitable in fields like ours where the mere existence of plotted data points is enough to give some scholars palpitations. The main thrust of their argument, however, is that complex, beautiful and awe-inspiring graphs are not inherently a bad thing when they are usable. If a graph is complex to the point of unreadability, that is usually because the graph-er was attempting a kind of elegant complexity and failed. (This, of course, returns us to one of the basic problems of DH: we’re doing things we were never trained to do and the success of being able to do them at all blinds us to the necessity of doing them well.)

Both pieces make certain assumptions that I think we, as the Ludic Analytics group, are not willing to make. The first is that visualizations exist to convey information to the reader and the second that visualizations must have some immediately identifiable utility. The visualization presented at the beginning of Crymble’s piece is meant as a joke, but because a) he doesn’t provide any more serious examples and b) the point I’m trying to make works just as well, I am going to pretend it is real and assume that if I can answer his reductio ad absurdum with logic, then said logic can surely be applied to more reasonable work. Like others of its ilk, this image a piece of art I would frame and hang on my wall rather than a readable graph. It offers very little in the way of interpretation to the untrained viewer and is, as Crymble says with his tongue firmly in his cheek, about 18th century cattle’s preference for south facing barns. Crymble is frustrated when asked to view graphs like this as proof. However, were there no image whatsoever–had he merely read a paper that claimed to have looked at the data and found that cattle preferred south-facing barns–I would imagine he would have had less trouble with the assertion. This visualization exists because it can, not because it makes any particular point. It is there to be beautiful. And were it real, it would also show that the researchers engaged with the data to the extent necessary to produce such a graph. It would not be proof of point, but proof of process. And I would imagine that the task of creating such a visualization and dealing with the information would give the researchers a better understanding of their data, even if the visualization lacks a trickle-down effect of understanding to the reader.

This brings me back to something I had discussed earlier, which is that our data always seems to be more useful for ourselves than for our readers. This may explain why the scholarly article and book have had such a long life; they don’t simply convey understanding, they enact it as well. Close reading recreates, in the article, the process through which we imbue texts with meaning. The act of applying historical research to a volume of literature mimics the act of research and the flash of understanding that comes when one grasps how a specific historical fact is relevant to the text at hand. Articles are processes, they are a temporal movement towards the end of an argument. Visualizations, however, lack that sense of journey. They are always, already, at the end even when you, the reader, are still at the beginning.

I can think of several possible solutions to this problem. One is to accompany visualizations with detailed descriptions of their genesis (Stephen Ramsay does this to good effect in his article “In Praise of Pattern”). Another is to create dynamic visualizations that can operate on a temporal as well as spatial scale. For example, imagine a social network graph where you can watch the edges build up between the different nodes while the nodes move around to create different groupings as the networks grow over the course of a novel. You could even have edges fade slightly if a connection has not been mentioned for over ten chapters, for example. As might be evident, I find this idea truly exciting and would love to imagine a novel performed as a network graph. A third option would be to use the visualization not as proof of theory, but as a starting point for the reader to form her own conclusions about the topic. The visualization becomes a way to share data rather than results and the reader is invited to tell her own story with it (I am drawing this idea from N. Katherine Hayles’s new book, How We Think). The data, and database, are an interface where textual exploration can happen rather than a static image of exploration someone else has already done.

These last two solutions require a somewhat radical rethinking of data presentation. Putting the visualization in as “(fig. 3)” on page 6 of the printed article is no longer going to cut it. Articles are very good at what they do, which is provide a forum in which to recreate traditional practice so that the reader can experience it along with the author. If we want our readers to experience our non-traditional readings along with us, we’re going to need non-traditional modes of delivery to do it.

Seeing and Doing

Before I begin, I’m going to apologize. I will not be present at the Research Slam and so I don’t get a chance to play with glitter. Or communicate my work on one large sheet of paper in a way that balances both an overall view of my research along with enough details to make it comprehensible and interesting.

Claire, Meaghan, further thoughts about poster-ing as a form of visualization that might help us for presentations later on?

I will, however, try to put together a presentation-esque page on this site for all those interested in what my work is. A virtual poster, if you will.

In the meantime, I’m going to talk about the act of making a visualization less useful. I realize this is not what we think of ourselves as doing, but the work I have been doing in Many Eyes has been leading me in this direction.

As you may have noticed, I’m the one a bit hung up on “but what will I use this for?” Despite trying to let go of this obsession, I still think of my visualizations in terms of use-value. And while the mere act of seeing anew that is at the heart of visualization always possesses this mix of dulce et utile, the balance between the two seems to shift depending on where I am in the process of creating multiple iterations of visualizations. Or, to put it another way, the further I push the visualizations associated with a given topic, the more I insight I gain into the text from the process even as my readers gain less insight from the final product than they would have from the original.

I’ll provide an example, and nicely circumvent the aforementioned problem by making you, O readers, a part of the process.

I began with one of the Word Tree visualizations in Many Eyes.

“Gwendolen Felt” in Tree Form

As before, feel free to click on the caption and play with the Visualization in Many Eyes. You can type anything you want into the search box and the word tree will move to show you all the sentences in the text that have that phrase and how they “branch” off. This dynamic visualization is eminently useful as a way to think about characters and get a quick glance at the different traits associated with them. I chose Gwendolen because the impression I got of the novel while reading it was that her feelings were the most interesting. And here they are.

Now I wanted to explore what exactly it was that Gwendolen felt. To do that, I returned to the one form of text analysis with which I think we’re all familiar; the “Find” function in Microsoft Word. I took all the sentences in the text where either “Gwendolen felt” or “She felt” in relation to Gwendolen appeared. I then sorted them into those sentences that used “felt” to refer to emotions or those that used it differently, such as referring to actual contact between two people. Then I color-coded each sentence based on whether the emotions were positive, negative or neutral/unclear.

The Good:

Gwendolen felt ready to manage her own destiny? Gwendolen felt daring. Gwendolen felt some strength.  She felt well equipped for the mastery of life. She felt quite sure of herself. She felt assured that she could act well. She felt satisfied with her prospects at Offendene. She felt kindly toward everybody and was satisfied with the universe. She felt as if she were reinforcing herself by speaking with this decisiveness to her uncle. She felt prepared to hear everything. She felt equal to arguing with him about her going on the stage. She felt able to maintain a dogged calm in the face of any humiliation that might be proposed. She felt an equal right to the Promethean tone. She felt at this moment that it was not likely she could ever have loved another man better than this one. She felt the more assured that her expectations of what was coming were right. 

The Bad:

Gwendolen felt the bitter tears of mortification rising and rolling down her cheeks. Gwendolen felt some anger with her mamma, but carefully concealed it. Gwendolen felt herself painfully in the position of the young lady who professed to like potted sprats. Gwendolen felt that the dirty paint in the waiting – room, the dusty decanter of flat water, and the texts in large letters calling on her to repent and be converted, were part of the dreary prospect opened by her family troubles; and she hurried away to the outer door looking toward the lane and fields. Gwendolen felt every word of that speech. Gwendolen felt that she was being weighed. Gwendolen felt a sinking of heart under this unexpected solemnity. Gwendolen felt a sudden alarm at the image of Grandcourt finally riding away. Gwendolen felt herself stricken. Gwendolen felt suddenly uncomfortable, wondering what was to come. She felt passionately averse to this volunteered love. She felt anew current of fear passing through her. She felt herself very far away from taking the resolve that would enforce acceptance. She felt shaken into a more alert attention, as if by a call to drill that everybody else was obeying. She felt a sort of numbness and could set about nothing. She felt a retrospective disgust for them. She felt compelled to silence. She felt her heart beating with a vague fear. She felt herself in an attitude of apology. She felt bashful about walking up to him and letting him know that she was there,.  She felt a peculiar anxiety to-day. She felt sick with irritation. She felt a little dispirited. She felt them to be insulting. She felt like a shaken child – shaken out of its wailing into awe. She felt some tingling bashfulness at the remembrance of her behavior towards him. She felt a rising rage against him mingling with her shame for herself. What she felt beside was a dull despairing sense. She felt her habitual stifling consciousness of having an immovable obstruction in her life. She felt herself reduced to a mere speck. She felt a peculiar vexation that her helpless fear had shown itself, not, as usual, in solitude, but in well-lit company. 

The Neutral:

Gwendolen felt this lot of unhoped-for fullness rounding itself too definitely. Gwendolen felt an inward shock. Gwendolen felt a contradictory desire to be hastened. Gwendolen felt as if her heart were making a sudden gambol. She felt quietly, unargumentatively sure. She felt something very far from indifference as to the impression she would make on him. Was it triumph she felt most or terror?

You can see several interesting things from these phrases. One is that Gwendolen is not a happy person. Two is that she is most definitely the “Spoiled Child” Eliot names her. The act of going through the text and pulling out these quotes one by one was fascinating. The process didn’t provide a whole new view of Gwendolen’s character, but it did create a portrait of her that seemed come to life differently than the one that appears over the course of the novel. It’s a slightly altered picture of both her vulnerabilities and her determination, especially as it takes no notice of any changes in her over the course of the novel. Though you might not get the same sense of her as you would if you read the book; this is more like a character sketch. They say a picture is worth a thousand words.

I was trying to figure out how to present this data other than using the link to the Many Eyes tree above and was inspired by Meaghan’s post with the images and texts. I couldn’t do that, but I wanted to try something similar.

For any of you suffering under the mistaken impression that I can freehand this, I should mention that I traced over the actress Romola Garai’s silhouette. As she played Gwendolen in the BBC miniseries adapted from Daniel Deronda, it seemed appropriate. (All work was done in Adobe Photoshop CS5.1, which I do not own, but which the Transcriptions lab at UCSB does.)

I think I will call this my ludic interpretation. Next to the Many Eyes Word Tree, it seems rather less informative, yet having gone through the process of visualizing it, I feel as though I have learned more than I would have simply by looking at the word tree.

As a personal project and a way to learn about a text and formulate views of characters, this was a great exercise. But how do I make it useful for anyone other than myself? I benefited from the process; the end result is interesting, but the true value lies in enacting. So how can we make visualizations that are as useful to our viewers as they are to us?