Authors: Guoshuai Ma, Hongren Yan, Yuhua Qian, Lingfeng Wang, Chuangyin Dang, Zhongying Zhao

Abstract:

Link prediction has received a great deal of attention from researchers. Most of the existing researches are based on the network topology but ignore the importance of its preference; for aggregating multiple pieces of information, they normally sum  up them directly. In this paper, a path-based probabilistic model is proposed to estimate the potential connectivity between  any two nodes. It takes carefully the efective infuence of nodes and the dependency among paths between two fxed nodes  into account. Furthermore, we formulate the connectivity of two inner-community nodes and that of two inter-community  nodes. The qualitative analysis shows that the links between inner-community nodes are more likely to be predicted by  the proposed model. The performance is verifed on both the multi-barbell network and Lesmis network. Considering the  proposed model's practicability, we develop an algorithm that iterates over the adjacent matrix to simulate paths of different lengths, with the parameters automatically grid-searched. The results of the experiments show that the proposed model  outperforms competitive methods.

Keywords： Link prediction; Preferential attachment; Community structure

Path-based estimation for link prediction.pdf

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Thu Jul 08 10:10:00 CST 2021