Link prediction algorithm based on clustering coefficient and node centrality

被引：0

作者：

Yu Y. ^{[1
,3
]}

Wang Y. ^{[1
]}

Luo Z. ^{[1
]}

Yang Y. ^{[1
]}

Wang X. ^{[1
]}

Gao T. ^{[2
]}

Yu Q. ^{[1
,3
]}

机构：

[1] School of Software, Yunnan University, Kunming

[2] School of Education, Yunnan University of Business Management, Kunming

[3] Key Laboratory in Software Engineering of Yunnan Province, Kunming

来源：

Qinghua Daxue Xuebao/Journal of Tsinghua University | 2022年 / 62卷 / 01期

关键词：

Clustering coefficient; Complex network; Link prediction; Node centrality;

D O I：

10.16511/j.cnki.qhdxxb.2021.21.039

中图分类号：

学科分类号：

摘要：

Currently, more people are becoming interested in the field of complex networks. Link prediction is a popular subdiscipline in complex networks and is used to predict missing links and identify false links. The traditional similarity-based complex network link prediction focuses on a particular similarity index of each node. This paper proposes the link prediction algorithm based on clustering coefficient and node centrality (CCNC), which combines the degree index, clustering coefficient index, and proximity centrality index into the link prediction of a complex network. This algorithm considers local information using clustering coefficient and degree by introducing proximity centrality to consider the importance of nodes in the network. Finally, using six real networks as examples, the feasibility and effectiveness of the CCNC algorithm are verified by comparing the AUC and the precision values. © 2022, Tsinghua University Press. All right reserved.

引用

页码：98 / 104

页数：6

共 25 条

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