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.
Highlights
Continuous research and exploration in network science have demonstrated that communities are widely existing structures in various real-world networks
This paper proposed an indicator for node communication behavior similarity in IP networks and constructed a community detection algorithm based on this indicator, which can obtain better results of community division than traditional methods
This paper addressed the problem of insufficient accuracy of current community detection methods for IP networks by proposing a novel indicator for evaluating the similarity of communication behaviors between IP nodes
Summary
Continuous research and exploration in network science have demonstrated that communities are widely existing structures in various real-world networks. Since the communication behaviors in the IP network are carried out in compliance with a certain protocol, such as http, email, etc., nodes that use the same network protocol to communicate will form a cluster according to the interaction and similarity in communication behaviors Motivated by this feature of IP network communication, this paper proposed a novel community detection method based on the similarity of communication behavior between IP nodes, which is measured using a newly proposed node similarity indicator. 2. The community detection method divides IP network based on the interactive relationships and behavioral similarities of IP nodes when collaborating or participating in the same service.
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