Crisis Image re-upload

Battles June 1918 The Crisis

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Gephi

scribner's.pngmurry.pnglewis.pngcrisis.pngboth.pngoverview.pngThe author and magazine that were the centers of influence were BLAST and Wyndham Lewis. Scribner’s magazine had a large center of influence as well but not as large as BLAST.

While authors had similar connections to the magazines, the magazines seemed to have greater importance. Especially in the case of Wyndham Lewis and BLAST, they have similar connections and are almost exactly the same. BLAST was the one that had more connections.

Murry from the Blue Review had few connections and edges. The Blue Review has more connections than Murry but it does shed light on the fact that they didn’t have a greater impact than some of the larger magazines.

One thing that was unexpected was the impact that the Crisis had didn’t have a large impact on what other people thought. If it did have a greater impact more people would be writing about it and would have more nodes. The amount of nodes from the Crisis was what caused further investigation. The nodes with the most connections are the art rather than the creator that made it. This means that people were more interested in the creation rather than the creator.

Done by Eli Jones, and William Grantham

Gephi

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I found that Gephi was not a particularly intuitive program to use. However, I did like how there were different ways to change what you are viewing, and its appearance. I think that given more time to experiment with it, I might like it more. The most interesting part of this was how it created a visual representation of the connections that the various points had. I do think that this was more usable when there weren’t as many points displayed at once. I liked seeing all of the connecting threads. I think that the information that could come from all of the data was interesting, but the initial display with all of the points shown was very overwhelming, and much less useful.

Using Gephi

First of all, using Gephi is not exactly intuitive. It is a very busy program, visually, and I think that might be a bit of a hinderance in learning what things the program is capable of when one is faced with a time limit. Our screenshots prove that there was a lot more we could have done with Gephi but did not have the time to figure out during class. Putting that aside, it is pretty interesting to use and we could see how it could be used on our final project.

There were many recognizable authors and magazines towards the center of the given network. A few of those authors included Theodore Roosevelt, T.S. Elliot, Wyndham Lewis, and Ezra Pound. Some of the larger noted woks were Scribner’s magazine, BLAST,and the Crisis just to name a few.

The listed authors and magazines, along with many more, gave off a sense that they were more important based on their size of their node in the network. In reality, this does not necessarily indicate their importance so much as their prominence in the data.

On a related note, the nodes that were small and far away from the center (indicating less prominence) were the least connected parts of the network. This means that they were used the least, in most cases. One example is “Royal Baking Powder”. Since the program completely foze the last ten minutes of class or so, we were unable to find what it was connected to other than “Royal”, “Baking”, and “Powder”, however, our guess is that it was probably an advertisement that was not heavily used. Maybe it was even only used once.

The network does not really indicate anything unexpect, but having such an interactive visualization helps to quantify and perceive the relations between authors, their works, and the types of work included in the magazines. It also helps to understand the dense, intertwined mass of connections across seemingly unrelated terms. It will definitely be a big help in the final project by giving us access to more objective information in a new way than what we have experienced thus far.

Screenshots:

Group members: Shelby Fields and Bethany Williams

Gephi Patterns

While Gephi seemed easy to start up, it definitely isn’t very user friendly. I may not supposed to be but perhaps I just don’t have enough knowledge about it’s features to make full use of it. I think the biggest problem I had was moving the graph around to get a closer a look at things. The zoom feature was a bit iffy too.

I didn’t recognize any of the nodes that were separated and far from the graph so I didn’t take any note of those. What I did notice was two important clusters.

The first cluster is of T.S. Eliot. It doesn’t look very extensive but compared to a lot of the nodes, this one had a lot attached! It was also close to the center. Obviously Eliot was one of the more important authors.

The second cluster is of the magazine BLAST. The node connections are very numerous. BLAST is connected to many of the modes, showing that it was one of the most important magazines in this graph.

Gephi

  1. Which authors and magazines are the centers of influence in this network?

BLAST is one of the largest influences. The Little Review and The New Age are also large in this graph. Not too many authors are very influential in this graph except for Windham Lewis.

screenshot_1458592. Do authors or magazines seem more important here?

Magazines definitely seem more important here. They appear much larger than almost any other thing in this graph entirely. The authors are scattered and are not nearly as large

3. In contrast, what magazines or authors are least connected, and what does that tell you about these data?

Some authors appear outside and have little to no connections. This tell me that these may have been one time authors or their other work has not been explored in this data set.

4. Does it show you anything unexpected? If so, what is it, and how did the graph help you to notice? If not, what is the value of the network graph as evidence, anyway?

Something unexpected that I found is, what seems to be, an ‘oops’ within the program. Such common words like “A, An, The” have some of the most connections in the graph. The graph helped me notice by enlarging these words and highlighting them in color.

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