Community Detection in Complex Networks using Genetic Algorithm

TL;DR

This paper presents a scalable genetic algorithm that optimizes network modularity for community detection in large complex networks without prior knowledge of community count or thresholds.

Contribution

It introduces a novel genetic algorithm approach that is efficient and scalable for large networks, outperforming existing methods in terms of complexity and applicability.

Findings

Achieves O(e) time complexity, suitable for large networks

Successfully detects communities in real-world datasets

Does not require prior knowledge of number of communities

Abstract

Community structure identification has been an important research topic in complex networks and there has been many algorithms proposed so far to detect community structures in complex networks, where most of the algorithms are not suitable for very large networks because of their time-complexity. Genetic algorithm for detecting communities in complex networks, which is based on optimizing network modularity using genetic algorithm, is presented here. It is scalable to very large networks and does not need any priori knowledge about number of communities or any threshold value. It has O(e) time-complexity where e is the number of edges in the network. Its accuracy is tested with the known Zachary Karate Club and College Football datasets. Enron e-mail dataset is used for scalability test.