EffGenPerm: An Efficient and Fast Generalized Community Detection for Massive Complex Networks

Abstract

The advances in community detection (CD) algorithms resulted in a study of analyzing the massive complex networks for resilience to perturbations. To address this issue, recently, few researchers had proposed CD algorithms by proposing new metrics like permanence and neighborhood connectivity. In this manuscript, we are proposing a new metric called “Effective Pull,” based on that an efficient CD algorithm has been developed, which will identify the underlying communities by maximizing effective permanence of a community by maximizing the effective permanence of each node in that community. As a peripheral output, our proposed algorithm fixes the drawbacks found in the recent advanced CD algorithms. The proposed is evaluated with real-time datasets and its efficiency is found better compared to recent literature.