Relatively important nodes mining algorithm based on community detection and biased random walk with restart

As modern network communication technology rapidly develops in recent years, complex networks have become a hot multidisciplinary research field. In this field, relatively important nodes mining is an emerging research topic with theoretical significance and application value. However, most researchers in the field of complex networks focus on sorting the global information in the network. Existing relatively important nodes mining algorithms commonly focus on the structural characteristics of the network and do not take into account the influence of community information on relatively important nodes mining. This paper addresses these problems by proposing a relatively important nodes mining algorithm based on community detection and biased random walk with restart (CDBRWR). This approach integrates the community information of the network into the mining of relatively important nodes for the first time and recommends a new biased random walk strategy with restart to realize the accurate and efficient mining of relatively important nodes in various networks. The performance of the proposed algorithm is examined through experimental verification and analysis of real network datasets. Results show that the CDBRWR algorithm outperforms other comparative algorithms in precision, recall, and AUC (area under the curve). (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this paper, a relatively important nodes mining algorithm based on community detection and biased random walk with restart (CDBRWR) is proposed. The algorithm incorporates community information into the mining of relatively important nodes, and introduces a new biased random walk strategy with restart to achieve accurate and efficient mining. Experimental verification and analysis show that the CDBRWR algorithm outperforms other comparative algorithms in terms of precision, recall, and AUC.