Finding Community Structure in Complex Networks Using Parallel Approach

Abstract

Network analysis is an important term in different scientific areas and finding the structure of communities is a significant challenge in network analysis. A group of vertices with high intra-connection and sparse inter-connection is called community. In this paper, we propose a novel method for community detection in networks, which works better in time and precision compared to similar methods. The proposed method is able to detect communities of a wide variety of networks with different properties. This method is an agglomerative parallel algorithm. Also it can find multiple communities and exchange the nodes between detected communities simultaneously. It has utilized local modularity for constructing the communities. After all, genetic algorithm is used to optimize the parameters of the proposed method. The algorithm is evaluated by modularity metric and shows a noticeable good precision. Also it has used simulated annealing to maximize the modularity.

Metaheuristic Multi-Objective Method to Detect Communities on Dynamic Social Networks

“…The detection of communities is considered as one of the basic methods in obtaining the hidden structure in the network. A lot of algorithms have been presented in last two decades to detect community structure which is a demanding task, including statistical inference [4][5][6], dynamic LP [7][8][9], spectral clustering [10], information-theoretic methods [11,12], topology based methods [13,14] and modularity optimization [15][16][17].…”