Bi-Objective Community Detection (BOCD) in Networks using Genetic Algorithm

TL;DR

This paper introduces a bi-objective genetic algorithm for community detection in networks that optimizes modularity and community score, demonstrating improved performance on benchmark and real-world datasets.

Contribution

The paper presents a novel bi-objective genetic algorithm that enhances community detection by simultaneously maximizing modularity and community score.

Findings

BOCD outperforms existing algorithms in modularity and MNI metrics

Effective detection of community structures in real and synthetic datasets

Improved community detection accuracy over previous methods

Abstract

A lot of research effort has been put into community detection from all corners of academic interest such as physics, mathematics and computer science. In this paper I have proposed a Bi-Objective Genetic Algorithm for community detection which maximizes modularity and community score. Then the results obtained for both benchmark and real life data sets are compared with other algorithms using the modularity and MNI performance metrics. The results show that the BOCD algorithm is capable of successfully detecting community structure in both real life and synthetic datasets, as well as improving upon the performance of previous techniques.