A comparison of agglomerative hierarchical algorithms for modularity clustering

Abstract

Modularity is a popular measure for the quality of a cluster partition. Primarily, its popularity originates from its suitability for community identification through maximization. A lot of algorithms to maximize modularity have been proposed in recent years. Especially agglomerative hierarchical algorithms showed to be fast and find clusterings with high modularity. In this paper we present several of these heuristics, discuss their problems and point out why some algorithms perform better than others. In particular, we analyze the influence of search heuristics on the balancedness of the merge process and show why the uneven contraction of a graph due to an unbalanced merge process leads to clusterings with comparable low modularity. © 2012 Springer-Verlag Berlin Heidelberg.