An efficient genetic algorithm for fuzzy community detection in social network

Abstract

A new fuzzy genetic algorithm proposed for community identification in social networks. In this paper, we have used matrix encoding that enables traditional crossover between individuals and mutation takes place in some of the individuals. Matrix encoding determines which node belongs to which community. Using these concepts enhance the overall performance of any evolutionary algorithms. In this experiment, we used the genetic algorithm with the fuzzy concept and compared to other existing methods like as crisp genetic algorithm and vertex similarity based genetic algorithm. We employed the three real world dataset strike, Karate Club, Dolphin in this work. The usefulness and efficiency of proposed algorithm are verified through the accuracy and quality metrics and provide a rank of proposed algorithm using multiple criteria decision-making method. © 2017, Springer Nature Singapore Pte Ltd.