Heuristics for Link Prediction in Multiplex Networks

TL;DR

This paper introduces a new framework and heuristics for link prediction in multiplex networks, leveraging connection type correlations, and demonstrates improved performance over traditional methods through experiments on various real-world networks.

Contribution

The paper presents a novel general framework and three heuristics specifically designed for multiplex network link prediction, addressing a gap in existing research.

Findings

Heuristics outperform baseline methods in multiplex networks.

Performance improves with richer connection type correlation.

Derived a theoretical threshold for connection type switching.

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.