Evaluating Community Detection Using a Bi-objective Optimization

Community detection is a topic of great interest in complex network analysis. The basic problem is to identify closely connected groups of nodes (i.e. the communities) from the networks of various objects represented in the form of a graph. Often, the problem is expressed as an optimization problem, where popular optimization techniques such as evolutionary computation techniques are utilized. The importance of these approaches is increasing for efficient community detection with the rapidly growing networks. The primary focus of this chapter is to study the applicability of such techniques for community detection. Our study includes the utilization of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) with their numerous variants developed specifically for community detection. We have discussed several issues related to community detection, GA, PSO and the major hurdles faced during the implication of evolutionary approaches. In addition, the chapter also includes a detailed study of how these issues are being tackled with the various developments happening in the domain.

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Evolutionary Computation Techniques for Community Detection in Social Network Analysis

Garg

Biswas

2016

Advanced Methods for Complex Network Analysis

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Evaluating Community Detection Using a Bi-objective Optimization

Cited by 1 publication

References 22 publications

Evolutionary Computation Techniques for Community Detection in Social Network Analysis

Evolutionary Computation Techniques for Community Detection in Social Network Analysis

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