In this report I rely on data on the time, user, and article edited on Wikipedia. From this data I generate temporal distances between edits in order to represent the edit chain of an article in a graph.
I tested the hypothesis that users and articles would develop into clusters: presumably around certain topics and areas of expertise. Comparing modularity metrics from k-means clustering and hierarchical clustering, hierchical had a much higher level at 0.7. Thus the network is more hierarchical rather than communal.