Finding Community Structure Based on Subgraph Similarity

TL;DR

This paper introduces a new subgraph similarity metric and algorithms for community detection that are faster and as reliable as existing methods, demonstrated through extensive experiments on real networks.

Contribution

It proposes a novel metric for structural similarity between subgraphs and develops efficient algorithms for community detection that outperform traditional methods in speed while maintaining quality.

Findings

The new algorithm matches the reliability of the CNM algorithm in modularity.

The proposed methods significantly reduce computational time.

Hybrid algorithm improves modularity and efficiency simultaneously.

Abstract

Community identification is a long-standing challenge in the modern network science, especially for very large scale networks containing millions of nodes. In this paper, we propose a new metric to quantify the structural similarity between subgraphs, based on which an algorithm for community identification is designed. Extensive empirical results on several real networks from disparate fields has demonstrated that the present algorithm can provide the same level of reliability, measure by modularity, while takes much shorter time than the well-known fast algorithm proposed by Clauset, Newman and Moore (CNM). We further propose a hybrid algorithm that can simultaneously enhance modularity and save computational time compared with the CNM algorithm.