Evolutionary Multi Objective Optimization Algorithm for Community Detection in Complex Social Networks
Shaik Tanveer ul Huq, Vadlamani Ravi, Kalyanmoy Deb

TL;DR
This paper introduces two novel three-objective evolutionary algorithms for community detection in social networks, demonstrating improved or comparable results to existing methods on benchmark datasets.
Contribution
It proposes two new multi-objective formulations using NSGA-III for community detection, incorporating novel objective functions and a ranking method for Pareto solutions.
Findings
The proposed algorithms outperform or match state-of-the-art methods.
Adding a third objective does not compromise the quality of solutions.
A new Pareto ranking measure based on hyper-volume and IGD is effective.
Abstract
Most optimization-based community detection approaches formulate the problem in a single or bi-objective framework. In this paper, we propose two variants of a three-objective formulation using a customized non-dominated sorting genetic algorithm III (NSGA-III) to find community structures in a network. In the first variant, named NSGA-III-KRM, we considered Kernel k means, Ratio cut, and Modularity, as the three objectives, whereas the second variant, named NSGA-III-CCM, considers Community score, Community fitness and Modularity, as three objective functions. Experiments are conducted on four benchmark network datasets. Comparison with state-of-the-art approaches along with decomposition-based multi-objective evolutionary algorithm variants (MOEA/D-KRM and MOEA/D-CCM) indicates that the proposed variants yield comparable or better results. This is particularly significant because the…
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Taxonomy
TopicsComplex Network Analysis Techniques · Data Visualization and Analytics
