Detecting highly overlapping community structure by greedy clique expansion

TL;DR

This paper introduces Greedy Clique Expansion (GCE), a new algorithm for detecting highly overlapping community structures in complex networks, outperforming existing methods especially when overlaps are extensive.

Contribution

GCE is a novel community detection algorithm that uses clique seeds and greedy expansion, effectively handling high levels of community overlap in various network types.

Findings

GCE performs well on synthetic graphs with extensive overlaps.

GCE outperforms existing algorithms in synthetic benchmarks.

GCE is competitive in real-world data applications.

Abstract

In complex networks it is common for each node to belong to several communities, implying a highly overlapping community structure. Recent advances in benchmarking indicate that existing community assignment algorithms that are capable of detecting overlapping communities perform well only when the extent of community overlap is kept to modest levels. To overcome this limitation, we introduce a new community assignment algorithm called Greedy Clique Expansion (GCE). The algorithm identifies distinct cliques as seeds and expands these seeds by greedily optimizing a local fitness function. We perform extensive benchmarks on synthetic data to demonstrate that GCE's good performance is robust across diverse graph topologies. Significantly, GCE is the only algorithm to perform well on these synthetic graphs, in which every node belongs to multiple communities. Furthermore, when put to the task of identifying functional modules in protein interaction data, and college dorm assignments in Facebook friendship data, we find that GCE performs competitively.