Advanced modularity-specialized label propagation algorithm for detecting communities in networks

TL;DR

This paper introduces LPAm+, an advanced community detection algorithm combining label propagation and greedy merging, achieving higher modularity in networks while balancing accuracy and computational efficiency.

Contribution

It presents a novel combination of label propagation and multistep greedy merging to improve community detection performance.

Findings

LPAm+ detects communities with higher modularity than previous methods.

LPAm+ balances accuracy and speed effectively.

Experiments on real-world networks validate the algorithm's superiority.

Abstract

A modularity-specialized label propagation algorithm (LPAm) for detecting network communities was recently proposed. This promising algorithm offers some desirable qualities. However, LPAm favors community divisions where all communities are similar in total degree and thus it is prone to get stuck in poor local maxima in the modularity space. To escape local maxima, we employ a multistep greedy agglomerative algorithm (MSG) that can merge multiple pairs of communities at a time. Combining LPAm and MSG, we propose an advanced modularity-specialized label propagation algorithm (LPAm+). Experiments show that LPAm+ successfully detects communities with higher modularity values than ever reported in two commonly used real-world networks. Moreover, LPAm+ offers a fair compromise between accuracy and speed.