SimBins: An information-theoretic approach to link prediction in real multiplex networks

TL;DR

SimBins is an information-theoretic method for link prediction in multiplex networks that leverages inter-layer correlations and link overlap to improve accuracy across diverse real-world datasets.

Contribution

It introduces a novel similarity-based approach, SimBins, which quantifies connection uncertainty and enhances prediction by incorporating inter-layer correlations and link overlap.

Findings

SimBins outperforms existing methods in accuracy across multiple datasets.

It is robust and applicable to various domains including social, biological, and technological networks.

The method has low computational overhead, suitable for large-scale networks.

Abstract

The entities of real-world networks are connected via different types of connections (i.e. layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method -- SimBins -- is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applied to various datasets from different domains, SimBins proves to be robust and superior than compared methods in majority of experimented cases in terms of accuracy of link prediction. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.