Detecting Communities in Networks by Merging Cliques

TL;DR

This paper introduces a novel community detection algorithm that begins with identifying disjoint cliques and then merges them to optimize modularity, resulting in improved accuracy and speed over existing methods.

Contribution

The paper presents a new clique-merging algorithm for community detection that outperforms existing greedy modularity optimization techniques in both effectiveness and efficiency.

Findings

Higher modularity scores achieved

Faster execution times compared to similar algorithms

Effective detection of densely connected communities

Abstract

Many algorithms have been proposed for detecting disjoint communities (relatively densely connected subgraphs) in networks. One popular technique is to optimize modularity, a measure of the quality of a partition in terms of the number of intracommunity and intercommunity edges. Greedy approximate algorithms for maximizing modularity can be very fast and effective. We propose a new algorithm that starts by detecting disjoint cliques and then merges these to optimize modularity. We show that this performs better than other similar algorithms in terms of both modularity and execution speed.