A multiobjective hybrid evolutionary algorithm for clustering in social networks

Authors:
Babak Amiri;Liaquat Hossain;John Crowford
Affiliations:
The University of Sydney, sydney, Australia;The University of Sydney, sydney, Australia;The University of Sydney, sydney, Australia
Venue:
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Year:
2012

Citing 8
Cited 0

Harmony search based algorithms for bandwidth-delay-constrained least-cost multicast routing

Computer Communications
Community detection in social networks with genetic algorithms

Proceedings of the 10th annual conference on Genetic and evolutionary computation
Effective Algorithm for Detecting Community Structure in Complex Networks Based on GA and Clustering

ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
GA-Net: A Genetic Algorithm for Community Detection in Social Networks

Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A Multi-objective Genetic Algorithm for Community Detection in Networks

ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
Community detection in complex networks using collaborative evolutionary algorithms

ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Harmony search in water pump switching problem

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
An Evolutionary Approach to Multiobjective Clustering

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

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.