Affiliations: [a] Specialized Data Mining Laboratory, Department of Computer Engineering, Alzahra University, Vanak, Tehran, Iran | [b] Department of Computer Engineering, Alzahra University, Tehran, Iran
Correspondence:
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Corresponding author: Maryam Pourkazemi, Department of Electronic, Computer and IT, Science and Research Branch, Islamic Azad University, Qazvin, Iran. E-mail:[email protected]
Abstract: Community detection is one of the main challenges in social network
analysis. Since the issue of community detection is considered as a NP-hard
problem, Evolutionary algorithms have been used as one of the most effective
approaches. In this paper, a multi-objective particle swarm optimization
algorithm and its extended versions are proposed. The aforementioned
algorithm uses an opposition-based method for producing an initial swarm. It
optimizes two objective functions at the same time which represents a
partition of the network as well as using a mutation operator for handling
the problem in high dimensions. The performances of the proposed algorithm
and its extended versions have been evaluated on real networks. The result
represented the efficiency of proposed methods. Also an optimum value is
suggested for aforementioned algorithm that can be said with less complexity
and calculations, proposed algorithm achieved the acceptable amount of
accuracy. The remarkable thing is the better performance of algorithm as the
size of social network grows.
Keywords: Community detection, social network, modularity, particle swarm optimization, multi-objective optimization, Pareto optimal, data mining
