Skip to Main content Skip to Navigation
Conference papers

A Modified Particle Swarm Optimization Algorithm for Community Detection in Complex Networks

Abstract : Community structure is an interesting feature of complex networks. In recent years, various methods were introduced to extract community structure of networks. In this study, a novel community detection method based on a modified version of particle swarm optimization, named PSO-Net is proposed. PSO-Net selects the modularity Q as the fitness function which is a suitable quality measure. Our innovation in PSO algorithm is changing the moving strategy of particles. Here, the particles take part in crossover operation with their personal bests and the global best. Then, in order to avoid falling into the local optimum, a mutation operation is performed. Experiments on synthetic and real-world networks confirm a significant improvement in terms of convergence speed with higher modularity in comparison with recent similar approaches.
Complete list of metadatas

Cited literature [36 references]  Display  Hide  Download
Contributor : Hal Ifip <>
Submitted on : Thursday, March 7, 2019 - 10:36:12 AM
Last modification on : Friday, March 8, 2019 - 1:23:51 AM
Long-term archiving on: : Saturday, June 8, 2019 - 1:33:03 PM


 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2021-01-01

Please log in to resquest access to the document


Distributed under a Creative Commons Attribution 4.0 International License



Alireza Abdollahpouri, Shadi Rahimi, Shahnaz Majd, Chiman Salavati. A Modified Particle Swarm Optimization Algorithm for Community Detection in Complex Networks. 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2018, Hamburg, Germany. pp.11-27, ⟨10.1007/978-3-319-99740-7_2⟩. ⟨hal-02060038⟩



Record views