Biased random walk with restart for link prediction with graph embedding method

Link prediction is an important problem in topics of complex networks, which can be applied to many practical scenarios such as information retrieval and marketing analysis. Strategies based on random walk are commonly used to address this problem. In common practice of a random walk, a link predictor may move from one node to one of its neighbors with uniform transferring probability regardless of the characteristics of the local structure around that node, which, however, may contain useful information for a successful prediction. In this paper, we propose a refined random walk approach which incorporates graph embedding method. This approach may provide biased transferring probabilities to perform random walk so as to further exploit topological properties embedded in the network structure. The performance of proposed method is examined by comparing with other commonly used indexes. Results show that our method outperforms all these indexes reflected by better prediction accuracy. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

In this paper, a refined random walk approach incorporating graph embedding method is proposed to provide biased transferring probabilities and exploit topological properties for link prediction in complex networks. The method outperforms commonly used indexes in terms of prediction accuracy.