I will be there for you: clique, character centrality, and community detection in Friends

Network science has proved useful in analyzing structure and dynamics of social networks in several areas. This paper aims at analyzing the relationships of characters in Friends, a famous sitcom. In particular, two important aspects are investigated. First, the structure of the communities (groups) is examined to shed light on how different methods for community detection perform. Second, besides investigating the static structure of the graphs as well as causality relationships, also temporal aspects were examined. After all, this show was aired for 10 years and thus plots, roles, and friendship patterns among the characters seem to have changed. Furthermore, this sitcom is frequently associated with distinguishing prior assumptions such as: all six characters are equally prominent; it has no dominant storyline; friendship as surrogate family. This paper uses tools from network theory to check whether these and other such assumptions can be quantified and proved correct, especially considering the temporal aspect, i.e., what happens in the sitcom along time. The main findings regarding the centrality and temporal aspects are: patterns in graphs representing different time slices of the show change; overall, degrees of the six friends are indeed nearly the same; however, contrarily to what is believed, in different situations, the magnitudes of degree centrality do change; betweenness centrality differs significantly for each character thus some characters are better connectors than others; there is a high difference regarding degrees of the six friends versus the rest of the characters, which points to a centralized network; there are strong indications that the six friends are part of a surrogate family. As for the presence of groups (communities) within the network, methods of different natures were investigated and compared (pairwise and also using various metrics, including plausibility). The multilevel method performs reasonably in general. Also, it stands out that those methods do not always agree, resulting in groups that are very different from method to method.