Community Detection Algorithm for Heterogeneous Networks Based on Central Node and Seed Community Extension

Abstract

In reality, most complex networks are heterogeneous and large-scale, they contain a variety of entity types and entity relationships, and their community structure often has the characteristics of overlap, complexity and diversity. The existing community detection algorithms do not fully consider the above characteristics, and the algorithm has low accuracy and large time complexity. In this paper, we study the community detection problem of large-scale heterogeneous complex networks based on general topology. We propose a multi-dimensional community detection algorithm Hete_M based on the community of central node, which can accurately detect the overlapping and heterogeneous communities of complex networks from multiple dimensions, has low time complexity and is suitable for large-scale heterogeneous complex networks.