On the Stability of Community Detection Algorithms on Longitudinal Citation Data

TL;DR

This paper investigates the stability of community detection algorithms on longitudinal citation networks, highlighting challenges due to their directed acyclic structure and providing guidance on method selection.

Contribution

It clarifies conditions for applying existing community detection methods to citation networks and offers insights to develop more targeted algorithms for longitudinal data.

Findings

Simulated data analysis reveals when existing methods are appropriate

Guidelines for selecting community detection algorithms in citation networks

Encourages development of specialized approaches for longitudinal citation data

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.