Community Detection in Complex Networks Using Genetic Algorithms

TL;DR

This paper introduces a genetic algorithm approach for community detection in complex networks that optimizes modularity without prior knowledge of community count, showing promising results on real and synthetic networks.

Contribution

The paper presents a novel genetic algorithm method for community detection that does not require pre-specified community numbers and outperforms some existing methods.

Findings

Effective detection of communities in real networks

Outperforms previous methods in some cases

Validated on synthetic networks with known structures

Abstract

Community detection is an important research topic in complex networks. We present the employment of a genetic algorithm to detect communities in complex networks which is based on optimizing network modularity. It does not need any prior knowledge about the number of communities. Its performance is tested on two real life networks with known community structures and a set of synthetic networks. As the performance measure an information theoretical metric variation of information is used. The results are promising and in some cases better than previously reported studies.