Though I haven't been able to do more exploring on Gephi since I don't have my own laptop, during the time we spent on it in class I was able to do a fair amount of exploring. It was interesting to see a visual representation of the themes in The Little Review  that we had discussed in class last week.  The visual map and nodes confirmed  that death was a key theme, if not the key theme of the magazine. Poetry and T.S. Eliot, not surprisingly,  in turn seemed to have a strong connection with the theme of death.  I also found it helpful to zero in on the connecting lines and their thickness or thinness. With little effort Gephi helped me see themes and connections that were strong in the magazine and also allowed me to see the smaller or more subtle connections and themes. I loved that Gephi provides this way of examining a large amount of text in the same screen view and offers a springboard for further study. 

Gephi, Graphs, and Ways to Read Magazines

I enjoyed the concept of Gephi, though the operationalization of it was tricky. I enjoyed seeing how the sea of words and cells from our timeline spreadsheet was turned into a map of sorts through the graphing tools, though I also found some of the limitations amusing (my program kept mapping TS and Eliot separately - no surprise, they had many shared edges!). It would be neat to see this program cleaned up and made more user-friendly.

I see one of Gephi's great strengths lying in the way it seems to help overcome some of the difficulties with reading magazines through their online PDF or screenshot instantiations. In Dr. Latham's "Unpacking My Digital Library" piece, he discusses how the common approach to reading a magazine is to flip through an issue, stopping at various articles/scriptons, maybe going through the piece a few times with different sequences, but not to do a linear reading progression from start to finish. The presentation of digitized versions of magazines we've been looking at lately has created an environment that tends to constrain the reader to start-to-finish reading; it's hard to flip through a PDF the way you can flip through a magazine. In contrast, Gephi makes it easy to hover over various nodes and look at their connections at will. In this way, I think Gephi helps restore in the digital realm an important element of and approach to reading magazines.

Thoughts on Gephi

While I still don't fully understand the program and probably never will, Gephi was really fun to play around with, and I actually found it easier to understand than some of the websites we've visited. I won't lie, though. At first I thought the placement of the nodes was completely random and had no idea what was going on. It was only when I experimented with coloring the nodes that I realized how they were related to one another. The layout and placement were extremely intricate, but I found that the more you played around with colors and themes, the easier it became to read. Obviously more general nodes like "poetry" were cluttered and highly populated while author's names were less connected. It surprised me that "death" was the most populated overall.

Once you understand how Gephi fuctions and how to best understand the correlation between the nodes, the program is a very helpful and interesting tool. I enjoyed using it.

The Nebula of Gephi

I have, unfortunately, been unable to use Gephi. I've uninstalled and reinstalled various versions of the beta - 7 and 8 - and it refuses to work. I hate to blame technology for something I could fix myself if I were more tech-savvy, but I'm pretty sure it keeps messing up because my computer runs on Vista.

That being said, I would like to discuss the idea of Gephi.

Gephi takes the vast world of literary analysis and compacts it into a tiny little nebula of information. Trends are turned into tiny planets and stars in the nebula that Gephi creates from each piece of work it reads. It takes information and data that would otherwise take hours to accrue, and consolidates them into easily-viewed "nodes" on its web graph. Looking at the graph itself is... different.

Personally, I have never studied literature in such a mathematical fashion, and, I'm going to be frank, it's weird to me. However, I do think it's necessary with the endlessly expanding universe of literature and knowledge. Without programs like Gephi, knowledge and information disappear into the abyss. As humbling a realization this is, it is impossible for humans to capture, analyze, and use every bit of knowledge we come across. As The Library of Babel and the literary philosophy of Derrida's Mal d'Archive posit, an archive has a "death drive". Constantly expanding to the point of disappearing into the margins, the vast and expanding oeuvre of mankind does not want to be known.

While I generally roll my eyes at people who think machines will supersede mankind, it is when I see programs like Gephi that I can sympathize a little with that paranoia. Humans just aren't good enough anymore. We create at a faster rate than we can analyze and archive, and efforts to become more efficient are made in vain. Gephi can gather up and read information, then preserve it in cryogenic stasis for man to further explore.

Experiences with Gephi

In the humanities, it’s always nice when technical, “objective” sources agree with what we’ve come to believe is true through more subjective interpretation. This is what happened with regards to our conclusion from last week that death is the major scripton of the September 1918 Little Review. Death has the highest degree of any terms, 60, while the average degree is 15.6. In some format that I happened upon (full disclosure, I have no idea how I got Gephi to do this), the size of the label corresponds to the degree of the item. Death, in comparison to the other items, is huge. Clearly, it is a key piece of this magazine.

Another interesting element of the network graph is pentagon/star formed by 5 major topics of The Little Review: Death, Poem, T.S. Eliot, Poetry, and Art. Each of these important scriptons (they all have a degree of at least 22) is connected to the others in the pentagon. Beyond emphasizing the importance of each of these scriptons, I’m not totally sure how to read this pentagon graphic, or even if it needs to be considered in greater depth.

Though I was initially quite frustrated with Gephi, I do think that it is a very cool program, especially for people who are visual learners. I’m constantly drawing up timelines, looking at maps, and drawing arrows in my notes because I love being able to physically see connections and relationships. Like a lot of things with technology, it’s a program that takes some practice, and I know my frustration was a product of my lack of knowledge, not the quality of the program. No, it’s not the most intuitive program, but it’s not terribly difficult figure out if you give yourself some time to read directions and just play around. I can definitely see myself using this program in the future, assuming I can learn how to create the data sets that form the backbone of the graph. 

"Reading" Gephi

I think that Gephi actually made it a bit more difficult to “read” the Little Review, but that’s probably because I don’t fully understand everything that the program can do and/or how to do it. It was helpful, though, to see how everything was connected because it wasn’t so obvious at first how they were, just reading it page by page. Something else that was really helpful/interesting was to see how you could isolate one of the nodes and it showed you what else was connected to that one, so you could see how one theme or author was represented throughout the magazine. I think it would be really cool if you could click on a node and see the actual journal page, kind of a mixture of the Modernist Journals Project and Gephi, and then all of the pages of the nodes that are linked to that one; that would allow you to “read” it through the graph, and to actually read it. Plus, it would make the issue’s themes easily searchable. (I tried to add screenshots, but they were not working for me.)


Exploring Gephi and Understanding Technology

After a long process of trying and failing to get Gephi to work on my computer, I played around with the program with Brooke and found so many cool things to explore! The visualization of the different genres, themes, and various people in the Little Review and how they related to each other was really helpful! I'm much more of a visual learner, so getting to see the visuals of how they were all connected, getting to move around the nodes and spread things out more was really fun.  I'm really excited to learn more about the program throughout this class!

In my quest to learn more about technology, I enlisted my dad to help me figure out what was going on (one very great side effect of going to school 30 minutes away from home).  He's been my lifeline to technology for the last 20 years of my life, but even The Great and Powerful Computer Wizard Dabney couldn't help me install Gephi on my laptop, so we dug my old laptop up and were able to get it installed on that computer.  I still don't quite understand what Java really does and why it's neccessary, or what it takes to run a program like Gephi, but I'm very slowly learning.  This long and drawn out adventure of installing Gephi on my laptop has been very helpful in that quest to understand technology, maybe someday I'll finally get there. 

Gephi and The Little Review

As I have been exploring the Gephi program, a question has been dominating my thoughts: Is this visual program useufl for extracting and analyzing information? Or is it simply a visual illustration? After playing with the program (mostly at random), I have discovered that it is a useful informational tool, but in a way much different than I expected. For instance, I was expecting the informatin to be some sort of explicit, numerical, statistical details. I understand that this information may in fact exist, and I am just incapable of finding it. However, the information I received came specifically from the visual representation. For instance, the web of nodes demonstrated what the core theme of the magazine was, as well as how various topics were related to one another through various works. Again, this isn't explicit information/data, but it is certainly helpful to know!

The Little Review: not so little, but finally getting littler

I have never used Gephi before this class, yet I already find myself taking a liking to it. Our assignment at the beginning of this week to "read" an entire issue of The Little Review was intimidating. Keep this in mind and imagine the difficulty of reading--of becoming well-read in--an entire field or category of works of literature. We face an incredible problem in our current digital age (and at that, one that particularly bothers me to the point that it's surprising that I am as big a computer and internet enthusiast as I am): the problem of information overload. It is not possible to process all of the information we have access to.

Although this problem is techically the same one we've always had, there is one crucial difference: now we have access to much more information--and instantly--and our ability to read it conventionally has not increased in proportion to our ability to obtain it. So here's where digital humanities--and particularly, a tool like Gephi--comes in. Gephi essentially provides a better way of scanning to me. I like how quickly I can home in on works within an issue that, for example, have to do with irony, and how I can adjust the degree of relatedness to "irony"  I am looking for. It seems poetic to me that if technology gives us greater information overload, technology must also give us a way to mitigate or even eliminate it once and for all.

Gephi & The Little Review (09/1918)


With the Yifan Hu algorithm, I further narrowed the graph by utilizing the Ego filter of “mediocrity.”  This graphing of the data provides some fairly straight forward connections, such as the connection of mediocrity to Yeats’s “In Memory of Robert Gregory” and the “Hades” episode of Ulysses.  However, there is also a less overt connection between mediocrity and World War I in the graph.  Given the general sentiment – I’m basing this both on our discussion of WWI in class and prior encounters with in other courses – of the epic scale of World War I and its wide felt reverberations throughout much of the world, a connection to mediocrity seems an interesting avenue for further investigation.  Because these two concepts – mediocrity and WWI – are connected by their shared relation to Yeats and Joyce, the graph created through the Yifan Hu algorithm gives an interesting means through which to (re)examine the discourse on death in the aftermath of WWI in the September 1918 issue of The Little Review.

Using the Fruchterman Reigold algorithm and again narrowing the graph’s spectrum to the Ego filter for “mediocrity,” the primary effect on my interpretation of the graph is less of a focus on the more prevalent topics, such as death or poetry.  With the centralization of the mediocrity node and the equivalent node and network web sizes, there is an opportunity to approach and interpret these various foci on a level playing field.  Because the graph that I created is more focused, this doesn’t seem to necessarily be the most productive algorithm to use, but I could see it being more significant for a graph of either a single issue or a few issues.

The network visualizations created by Gephi do seem to be a fairly useful tool in periodical studies, as they allow for interesting connections both within a single issue and (conceivably) among a series of issues.  The collaborative nature of this enterprise seems to be both the greatest advantage and disadvantage of this sort of analysis.  In joining the input of a multitude of scholars, network visualization graphs provide an exciting avenue for future scholarship, not only in sheer breadth of material that can be covered but also in the creation of new connections through the polyvocality of such a collaboration.  However, there do seem to be some drawbacks in the logistics of such a project.  The clear example from the graph constructed in this lab is the non-uniform tags, which create redundancies in the graph – the best example is “Poem” and “Poetry.”  While this is a seemingly minor issue with such a small sample size, one issue, it would seem to exponentially compound itself as the scope of such a project grew.  Erin further discusses issues with tagging in her blog post this week.