Link prediction via significant influence

YujieYang, ZHANG Jianhua, Xuzhen Zhu, Lei Tian
Physica A: Statistical Mechanics and its Applications, 2018 ( Vol.492, Pages 1523-1530 )

Publish Year: 2018,

Abstract: In traditional link prediction, many researches assume that endpoint influence, represented by endpoint degree, prefers to facilitate the connection between big-degree endpoints. However, after investigating the network structure, it is observed that influence is determined by the relations built through the paths between endpoints instead of the endpoint degree. Strong relations connecting the other endpoint through short paths, especially through common neighbors, can bring in more powerful influence, and in contrast, those relations through long paths obviously generate weak influence. In this paper, a novel link prediction index SI is proposed, which deliberately models the significant influence by distinguishing the strong influence from the weak. After comparison with main stream baselines on 12 benchmark datasets, the results suggest SI effectively improve the link prediction accuracy.

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