Link Prediction Model for Weighted Networks Based on Evidence Theory and the Influence of Common Neighbours

Author:

Liu Miaomiao12ORCID,Wang Yang1ORCID,Chen Jing3ORCID,Zhang Yongsheng1ORCID

Affiliation:

1. School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, Heilongjiang, China

2. Key Laboratory of Petroleum Big Data and Intelligent Analysis of Heilongjiang Province, Daqing 163318, Heilongjiang, China

3. College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China

Abstract

A link prediction model for weighted networks based on Dempster–Shafer (DS) evidence theory and the influence of common neighbours is proposed in this paper. First, three types of future common neighbours (FCNs) and their topological structures are proposed. Second, the concepts of endpoint weight influence, link weight influence, and high-strength node influence are introduced. Then, the similarity based on the impacts of current common neighbours (CCNs) and FCNs is defined, respectively. Finally, the two similarity indices are fused by the DS evidence theory. This model effectively integrates multisource information and completely exploits the influence of all CCNs and FCNs on similarity. Experiments are performed on 9 real and 40 simulation-weighted datasets, and these findings are compared with several classic algorithms. Results show that the proposed method has higher precision than other methods, which can achieve good performance in link prediction in weighted networks.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

Reference45 articles.

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