This week I’ve created a network graph for a short fiction “The New Me”, a story told by a first-person narrative which in this graph, I assumed her to be the author of the article, Halle Butler. The story tells a snippet of Halle’s mundane life as she tries to make a plan and talk to one of her “friend”, Sarah, via coffee and pancakes. However, Sarah turned down the plan with an excuse for not having money, and instead, she brought over beers to her apartment during the night. The night turned out to be terrible for Halle due to Sarah’s constant complaints and negativity throughout the night. The next day, a hungover Halle was wandering around the park near her apartment and she encountered an unfriendly park ranger. After a passive-aggressive conversation, Halle left and tried to get a spiced hot cider.
Below is a screenshot of the network graph I’ve created through Google Fusion (click here for an interactive version):
The network graph shows a very basic connection between the characters mentioned in the story. By analyzing the graph, we can see that Halle and Sarah are the central nodes in the story. On top of that, we can also visualize how the other side characters are connected to these two main characters. Aside from the surface information, this graph cannot really display a more meaningful relationship between the characters. For instance, it cannot show whether a certain connection is stronger/ weaker, positive/negative etc. In this graph, there is no way that someone can deduce the complicated and conflicting relationship between Halle and Sarah, nor can we even tell what kind of relationship Sarah has with Chris. Thus, I believe a Google Fusion is insufficient in forming a meaningful network graph for the study of humanities, and there should be more complex functions that allow users to color-code, use different lines to differentiate different kinds of relationships.