On the Stability of Community Detection Algorithms on Longitudinal Citation Data

There are fundamental differences between citation networks and other classes of graphs. In particular, given that citation networks are directed and acyclic, methods developed primarily for use with undirected social network data may face obstacles. This is particularly true for the dynamic development of community structure in citation networks. Namely, it is neither clear when it is appropriate to employ existing community detection approaches nor is it clear how to choose among existing approaches. Using simulated citation data, we highlight the tradeoff inherent in algorithm selection thereby clarifying the conditions under which one should use existing methods. We hope this paper will serve as encouragement for those interested in the development of more targeted approaches for use with longitudinal citation data.