Cold-start link prediction in multi-relational networks based on network dependence analysis

Shun-yao Wu, Qi Zhang, Chuan-yu Xue and Xi-yang Liao

Physica A: Statistical Mechanics and its Applications, 2019, vol. 515, issue C, 558-565

Abstract: Cold-start link prediction has been a hot issue in complex network. Different with most of existing methods, this paper utilizes multiple interactions to predict a specific type of links. In this paper, multiple interactions are abstracted as multi-relational networks, and robust principle component analysis is employed to extract low-dimensional latent factors from sub-networks. Then a distribution free independence test, projection correlation, is introduced to efficiently analyze dependence between target and auxiliary sub-networks. Furthermore, associated auxiliary networks are exploited for cold-start link prediction, which aims to forecast potential links for new/isolated nodes in target sub-networks. Experimental results on 8 bioinformatics datasets validate rationality and effectiveness of the method.

Keywords: Cold-start link prediction; Network dependence analysis; Projection correlation statistics; Robust principle component analysis (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:515:y:2019:i:c:p:558-565

DOI: 10.1016/j.physa.2018.09.082

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