Community Detection in Social Graph Using Nature-Inspired Based Artificial Bee Colony Algorithm with Crossover and Mutation

Abstract

Many types of social network are modelled as graphs. Community detection has been an important research area in social graph analysis. Community detection can be viewed as an optimization problem. Nowadays, researchers use nature-inspired algorithms to solve optimization problem. Their goal is to find the optimal solution for a given problem. In this paper, nature-inspired based artificial bee colony algorithm with crossover and mutation is used to detect community in social graphs. GraphX is built as a library on the top of Spark by encoding graph as a collection of vertices and edges. Comparative studies describe that the proposed algorithm and other nature-inspired algorithms can effectively detect the community structure on real world social graphs as other traditional community detection algorithms.