Title: Link prediction in multilayer networks

Authors: Deepak Malik; Anurag Singh

Addresses: Department of Computer Science and Engineering, National Institute of Technology Delhi, Delhi, India ' Department of Computer Science and Engineering, National Institute of Technology Delhi, Delhi, India

Abstract: Link prediction has gained popularity in recent years in large networks. Researchers have proposed various methods for finding the missing links. These methods include common neighbour, Jaccard coefficient, etc. based on the proximity of the nodes. These methods have limitations as they treat all common nodes equal from a pair of nodes. A new method is proposed, common neighbour's common neighbour (CNCN). Its performance is better than the existing methods in a single layer network. These methods are based on the topological features of the network. The proposed method finds the different behaviour of common nodes for a pair of nodes. The link prediction is also useful in the multiplex networks. The link predictions in the multiplex networks are more useful than the single layer network as several layers may give more information about a node than the single layer network. Two methods are proposed using dynamic and static weights.

Keywords: common neighbours; complex network; link prediction.

DOI: 10.1504/IJBIDM.2020.107547

International Journal of Business Intelligence and Data Mining, 2020 Vol.16 No.4, pp.490 - 505

Received: 12 Jul 2017
Accepted: 30 Oct 2017
Published online: 01 Jun 2020 *

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