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A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model

Software College, Northeastern University, Shenyang 110169, China
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Symmetry 2020, 12(1), 100; https://doi.org/10.3390/sym12010100
Received: 19 December 2019 / Revised: 31 December 2019 / Accepted: 2 January 2020 / Published: 5 January 2020
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous achievements, whereas suffering from incapability of dealing with dynamic behaviors and low predicting accuracy. Aiming at addressing the problem, this paper employs a diagonally symmetrical supra-adjacency matrix to represent the dynamic social networks, and proposes a temporal links prediction framework combining with an improved gravity model. Extensive experiments on several real-world datasets verified the superiority on competitors, which benefits recommending friends in social networks. It is of remarkable significance in revealing the evolutions in temporal networks and promoting considerable commercial interest for social applications. View Full-Text
Keywords: social network; temporal links prediction; gravity model; multilayer network social network; temporal links prediction; gravity model; multilayer network
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Huang, X.; Chen, D.; Ren, T. A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model. Symmetry 2020, 12, 100.

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