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arXiv · 0908.0449

On the Stability of Community Detection Algorithms on Longitudinal Citation Data

Michael James Bommarito II·Daniel Martin Katz·Jon Zelner
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Abstract

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 data, we attempt to clarify the conditions under which one should use existing methods and which of these algorithms is appropriate in a given context. We hope this paper will serve as both a useful guidepost and an encouragement to those interested in the development of more targeted approaches for use with longitudinal citation data.

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Michael James Bommarito II, Daniel Martin Katz, Jon Zelner. 2009-08-17. On the Stability of Community Detection Algorithms on Longitudinal Citation Data. https://arxiv.org/abs/0908.0449

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