The Comprehensive Contributions of Endpoint Degree and Coreness in Link Prediction. (11th August 2021)

• Record Type: Journal Article
• Authors: Tian, Yang; Wang, Yanan; Tian, Hui; Cui, Qimei
• Other Names: Xiong Fei Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-08-11
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/1544912
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : In past studies, researchers find that endpoint degree, H-index, and coreness can quantify the influence of endpoints in link prediction, especially the synthetical endpoint degree and H-index improve prediction performances compared with the traditional link prediction models. However, neither endpoint degree nor H-index can describe the aggregation degree of neighbors, which results in inaccurate expression of the endpoint influence intensity. Through abundant investigations, we find that researchers ignore the importance of coreness for the influence of endpoints. Meanwhile, we also find that the synthetical endpoint degree and coreness can not only describe the maximal connected subgraph of endpoints accurately but also express the endpoint influence intensity. In this paper, we propose the DCHI model by synthesizing endpoint degree and coreness and the HCHI model by synthesizing H-index and coreness on SRW-based models, respectively. Extensive simulations on twelve real benchmark datasets show that, in most cases, DCHI shows better prediction performances in link prediction than HCHI and other traditional models.