Link prediction based on contribution of neighbors

Xiang-Chun Liu (), Dian-Qing Meng, Xu-Zhen Zhu () and Yang Tian ()
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Xiang-Chun Liu: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China
Dian-Qing Meng: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China
Xu-Zhen Zhu: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China
Yang Tian: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China

International Journal of Modern Physics C (IJMPC), 2020, vol. 31, issue 11, 1-10

Abstract: Link prediction based on node similarity has become one of the most effective prediction methods for complex network. When calculating the similarity between two unconnected endpoints in link prediction, most scholars evaluate the influence of endpoint based on the node degree. However, this method ignores the difference in contribution of neighbor (NC) nodes for endpoint. Through abundant investigations and analyses, the paper quantifies the NC nodes to endpoint, and conceives NC Index to evaluate the endpoint influence accurately. Extensive experiments on 12 real datasets indicate that our proposed algorithm can increase the accuracy of link prediction significantly and show an obvious advantage over traditional algorithms.

Keywords: Complex network; link prediction; contribution of neighbors; random walk (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0129183120501582

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