Community detection based on similarities of communication behavior in IP networks

Abstract

Communities are an important feature of real-world networks that can reveal the structure and dynamic characteristics of networks. Accordingly, the accurate detection and analysis of the community structure in large-scale IP networks is highly beneficial for their optimization and security management. This paper addresses this issue by proposing a novel community detection method based on the similarity of communication behavior between IP nodes, which is determined by analyzing the communication relationships and frequency of interactions between the nodes in the network. On this basis, the nodes are iteratively added to the community with the highest similarity to form the final community division result. The results of experiments involving both complex public network datasets and real-world IP network datasets demonstrate that the proposed method provides superior community detection performance compared to that of four existing state-of-the-art community detection methods in terms of modularity and normalized mutual information indicators.