A new link prediction in multiplex networks using topologically biased random walks

Elahe Nasiri, Kamal Berahmand and Yuefeng Li

Chaos, Solitons & Fractals, 2021, vol. 151, issue C

Abstract: Link prediction is a technique to forecast future new or missing relationships between nodes based on the current network information. However, the link prediction in monoplex networks seems to have a long background, the attempts to accomplish the same task on multiplex networks are not abundant, and it was often a challenge to apply conventional similarity methods to multiplex networks. The issue of link prediction in multiplex networks is the way of predicting the links in one layer, taking structural information of other layers into account. One of the most important methods of link prediction in a monoplex network is a local random walk (LRW) that captures the network structure using pure random walking to measure nodes similarity of the graph and find unknown connections. The goal of this paper is to propose an extended version of local random walk based on pure random walking for solving link prediction in the multiplex network, referred to as the Multiplex Local Random Walk (MLRW). We explore approaches for leveraging information mined from inter-layer and intra-layer in a multiplex network to define a biased random walk for finding the probability of the appearance of a new link in one target layer. Experimental studies on seven multiplex networks in the real world demonstrate that a multiplex biased local random walk performs better than the state-of-the-art methods of link prediction and corresponding unbiased case and improves prediction accuracy.

Keywords: Complex networks; Multiplex networks; Link prediction; Random walk; Layer relevance (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:151:y:2021:i:c:s0960077921005841

DOI: 10.1016/j.chaos.2021.111230

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