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

Yinzuo Zhou, Chencheng Wu and Lulu Tan

Physica A: Statistical Mechanics and its Applications, 2021, vol. 570, issue C

Abstract: 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.

Keywords: Link prediction; Graph embedding method; Random walk with restart; Biased transferring probability (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:570:y:2021:i:c:s0378437121000558

DOI: 10.1016/j.physa.2021.125783

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