Journal Article10.1016/j.physa.2022.128219
Relatively important nodes mining algorithm based on community detection and biased random walk with restart
Qian Liu,Jia Wang,Zhi-Gang Zhao,Na Zhao +3 more
- 01 Sep 2022
Vol. 607, pp 128219-128219
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TL;DR: Wang et al. as discussed by the authors proposed a relatively important nodes mining algorithm based on community detection and biased random walk with restart (CDBRWR), which integrates the community information of the network into the mining of relatively important node for the first time and recommends a new biased Random Walk strategy with restart to realize the accurate and efficient mining of relative important nodes in various networks.
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Abstract: 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). • CDBRWR makes the best of the network nodes’ community information to obtain the network’s topology characteristics. • CDBRWR adopts the biased random walk strategy with restart to acquire the global information of the entire network. • CDBRWR is a superior candidate for quantifying the relative importance of nodes in the network. • A simple yet clear research framework about how to identify relatively important nodes.
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References
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Vincent D. Blondel,Jean-Loup Guillaume,Jean-Loup Guillaume,Renaud Lambiotte,Renaud Lambiotte,Etienne Lefebvre +5 more
TL;DR: This work proposes a heuristic method that is shown to outperform all other known community detection methods in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.
Community structure in social and biological networks
Michelle Girvan,Mark Newman +1 more
TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Finding and evaluating community structure in networks.
TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
node2vec: Scalable Feature Learning for Networks
Aditya Grover,Jure Leskovec +1 more
- 13 Aug 2016
TL;DR: Node2vec as mentioned in this paper learns a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes by using a biased random walk procedure.
•Posted Content
node2vec: Scalable Feature Learning for Networks
Aditya Grover,Jure Leskovec +1 more
TL;DR: In node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks, a flexible notion of a node's network neighborhood is defined and a biased random walk procedure is designed, which efficiently explores diverse neighborhoods.
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