Improviesed Genetic Algorithm for Fuzzy Overlapping Community Detection in Social Networks
Community structure identification is an important task in social network analysis. Social
communications exist with some social situation and communities are a fundamental form of social contexts.
Social network is application of web mining and web mining is also an application of data mining. Social
network is a type of structure made up of a set of social actors like as persons or organizations, sets of pair ties,
and other interaction socially between actors. In recent scenario community detection in social networks is a
very hot and dynamic area of research. In this paper, we have used improvised genetic algorithm for community
detection in social networks, we used the combination of roulette wheel selection and square quadratic knapsack
problem. We have executed the experiment on different datasets i.e. Zachary’s karate club , American
college football , Dolphin social network  and many more. All are verified and well known datasets in
the research world of social network analysis. An experimental result shows the improvement on convergence
rate of proposed algorithm and discovered communities are highly inclined towards quality.
Index term: community detection, genetic algorithm, roulette wheel selection,