Community Detection Using Nature Inspired Algorithm

Community detection in social networks has become a dominating topic of the current data research as it has a direct implication in many different areas of importance, whether social network, citation network, traffic network, metabolic network, protein-protein network or web graph etc. Mining meaningful communities in a real-world network is a hard problem owing to the dynamic nature of these networks. The existing algorithms for community detection depend chiefly on the network topologies and are not effective for such sparse graphs. Thus, there is a great need to optimize those algorithms. Evolutionary algorithms have emerged as a promising solution for optimized approximation of hard problems. We propose to optimize the community detection method based on modularity and normalized mutual information (NMI) using the latest grey wolf optimization algorithm. The results demonstrate the effectiveness of the algorithms, when compared with other contemporary evolutionary algorithms.

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