A Streaming-based Algorithm for Overlapping Community Detection

Abstract

Mining the overlapping community structure is a very important task in complex network analysis. With the development of social network and Internet of things, the scale of data is becoming larger and larger. Many existing algorithms can not deal with such a large-scale network. In order to improve the efficiency of community detection in large-scale networks, an overlapping community detection algorithm based on graph streaming is proposed. The algorithm processes the edges in the network in a streaming mode, processes only one edge at a time and each edge is processed only once and then discarded. The algorithm divides the nodes according to the degree of node, the contribution of node to the community, and the number of connected edges between communities before and after the node's moving. Experimental results on real-life networks show that our proposed approach can quickly discover overlapping community structures in large-scale networks with linear computational complexity.