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Abstract
Social network analysis concerns with modeling network dynamics to find repeated pattern, centrality analysis that aims to identify the most important of nodes in networks, influence modeling that aims to understand the process of influence or information diffusion, and community detection that is concerned with finding structure in networks. Finding a community in a social network is to identify a set of nodes such that they interact with each other more frequently than with those nodes outside the group. Detecting cohesive groups in a social network remains a core problem in social network analysis.Community detection can be viewed as an optimization problem in which an objective function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. Many single-objective optimization techniques have been used to solve the detection problem. Over the years many quality measures of community have been proposed and have been used as objective functions in the optimization process such as Modularity. from this perspective we first use Genetic algorithms as an effective optimization technique to solve the community detection problem using some popular quality measures that have been used widely in the literature