Heuristics for Link Prediction in Multiplex Networks

• URL: http://arxiv.org/abs/2004.04704v1
• Date: Thu, 9 Apr 2020 17:36:18 GMT
• Title: Heuristics for Link Prediction in Multiplex Networks
• Authors: Robert E. Tillman, Vamsi K. Potluru, Jiahao Chen, Prashant Reddy, Manuela Veloso
• Abstract summary: We propose a novel framework and three families of families for multiplex network link prediction. We show that the proposeds show increased performance with the richness of connection type correlation structure and significantly outperform their baselines for ordinary networks with a single connection type.
• Score: 15.637077180633735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Link prediction, or the inference of future or missing connections between entities, is a well-studied problem in network analysis. A multitude of heuristics exist for link prediction in ordinary networks with a single type of connection. However, link prediction in multiplex networks, or networks with multiple types of connections, is not a well understood problem. We propose a novel general framework and three families of heuristics for multiplex network link prediction that are simple, interpretable, and take advantage of the rich connection type correlation structure that exists in many real world networks. We further derive a theoretical threshold for determining when to use a different connection type based on the number of links that overlap with an Erdos-Renyi random graph. Through experiments with simulated and real world scientific collaboration, transportation and global trade networks, we demonstrate that the proposed heuristics show increased performance with the richness of connection type correlation structure and significantly outperform their baseline heuristics for ordinary networks with a single connection type.

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