A multiobjective hybrid evolutionary algorithm for clustering in social networks

Amiri, B., Hossain, L. and Crawford, J. W. (2012) A multiobjective hybrid evolutionary algorithm for clustering in social networks. In: 14th Annual Conference Companion on Genetic and Evolutionary Computation, Philadelphia, PA, USA, July 7–11, 2012, pp. 1445-1446. ISBN 9781450311786 (doi: 10.1145/2330784.2330981)

Abstract

Detecting community structure is crucial for uncovering the links between structures and functions in complex networks. Most of contemporary community detection algorithms employ single optimization criteria (e.g., modularity), which may have fundamental disadvantages. This paper considers the community detection process as a Multi-Objective optimization Problem (MOP). To solve the community detection problem this study used modified harmony search algorithm (HAS), the original HAS often converges to local optima which is a disadvantage with this method. To avoid this shortcoming the HAS was combined with a Chaotic Local Search (CLS). In the proposed algorithm an external repository considered to save non-dominated solutions found during the search process and a fuzzy clustering technique was used to control the size of the repository. The experiments in synthetic and real networks show that the proposed multi-objective community detection algorithm is able to discover more accurate community structures.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Crawford, Professor John
• Authors: Amiri, B., Hossain, L., and Crawford, J. W.
• College/School: College of Social Sciences > Adam Smith Business School > Management
• Journal Name: GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion
• ISBN: 9781450311786