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However, neither endpoint degree nor H\u2010index 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\u2010index and coreness on SRW\u2010based 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.<\/jats:p>","DOI":"10.1155\/2021\/1544912","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T17:20:10Z","timestamp":1628702410000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The Comprehensive Contributions of Endpoint Degree and Coreness in Link 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