I created a network graph based on the short story “Martha, Martha” by Zadie Smith. In the story, a real estate agent named Pam Roberts shows a client, Martha Penk, two properties before Martha decides not to purchase either of them. Pam Roberts is a middle-aged woman from the Midwest who is generally good-natured, but is fond of gossip and sometimes expresses xenophobic sentiments. Having arrived in Massachusetts a week ago, Martha has unrealistic notions about the kinds of properties she can afford. She appears to have been part of the working class in England, but hopes to attend a university and to cultivate cultural knowledge. She continually exhibits abrupt and rude behavior, leading Pam to conclude that she is odd and somewhat uncivil. By the end of the story, however, it appears that Martha’s behavior is the result of emotional turmoil. She seems to have left her son and his father in order to chase her academic dreams, and her anguish at this seems to explain why she abruptly declines to purchase and leaves the second property.

There are 22 nodes on my network graph, each signifying a character in the story. I considered a connection between characters to constitute whether one character had spoken to, or about, another character. For instance, while Pam Roberts speaks to Martha, Amelia, and “Middle-Easterny” Man 1, she does not interact directly with most of the characters during the story. While it is implied that she has spoken to each of the people she mentions at some point, she demonstrates most of her relationships to people by sharing gossip about these people rather than actually interacting with them.
The way I formatted this graph is somewhat confusing in that there is no visual distinction between relationships demonstrated by direct interactions and relationships that a character only claims to exist. In one sense, I view this as a limitation of this network graph. However, it can also become an aid to understanding the story in that the same kind of confusion between knowing a person and knowing about a person occurs in the story. It is clear that whether Pam Roberts interacts with a person, or only describes a previous interaction, she views everyone she interacts with as little more than a source of gossip. Though Pam speaks with Yousef’s wife, Amelia, and only describes the Professor’s wife, both wives appear equally two-dimensional because Pam’s assumptions entirely define them. Pam’s assumptions about Martha, though unfounded, even begin to affect how Yousef and Amelia view her.
No one truly knows anyone in this story, and so every relationship’s value is limited. For this reason, this network graph is deceptive in another key way. Although Pam Roberts has the most connections, none of them are particularly deep or meaningful. She deems a man she meets to sound “Middle-Easterny” and does not even ask him his name (she applies the same assumption to his three companions). Even when Pam mentions her three daughters, the reference is fleeting and not necessarily fond. Yet Martha, who has fewer connections and seems disagreeable for most of the story, actually has two very deep connections in Ben and Jamal. The number of connections is not at all indicative of their depth—in fact, this seems to be an inverse relationship.
Note: though not technically a “character,” I included the Snowman as a node because it is a helpful focal point for viewing the relationships between the four men.
I really loved how you addressed the lack of a visual distinction between direct and implicit relationships of the characters in the story you read and how this could actually illuminate the confusion between knowing a person and simply knowing about them. Although I did not read “Martha, Martha” I can understand that gossip is an underlying theme of the story, and I like how your network graph is able to adhere and showcase that theme, for no one truly knows one another in the story.
I liked how you clearly identified the nodes as I can see the relationships directly. I also enjoyed the narrative you supplied to the graph. Great Job!