Open Access

ARTICLE

Multi-Objective Evolutionary Framework for High-Precision Community Detection in Complex Networks

Asal Jameel Khudhair^#, Amenah Dahim Abbood^#,*

Department of Computer Science, College of Science, University of Baghdad, Al-Jadriya Area, Baghdad, 10070, Iraq

* Corresponding Author: Amenah Dahim Abbood. Email:
# These authors contributed equally to this work

(This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)

Computers, Materials & Continua 2026, 86(1), 1-31. https://doi.org/10.32604/cmc.2025.068553

Received 31 May 2025; Accepted 28 August 2025; Issue published 10 November 2025

Abstract

Community detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities. Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can. Therefore, we utilized two objective functions that enable strong connections between communities and weak connections between them. In this study, we utilized the intra function, which has proven effective in state-of-the-art research studies. We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse. Furthermore, we proposed a Multi-Objective community strength enhancement algorithm (MOCSE). The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition (MOEA/D), integrated with a new heuristic mutation strategy, community strength enhancement (CSE). The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient. The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information (NMI) and modularity (Q). It was tested using five state-of-the-art algorithms on social networks, comprising real datasets (Zachary, Dolphin, Football, Krebs, SFI, Jazz, and Netscience), as well as twenty synthetic datasets. These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.

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Keywords

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