Overlapping Communities and the Prediction of Missing Links in Multiplex Networks

TL;DR

This paper introduces ML-BNMTF, a novel link prediction method for multiplex networks that leverages overlapping community structures across layers, improving prediction accuracy especially in low-overlap scenarios.

Contribution

It proposes a new method, ML-BNMTF, that utilizes inter-layer community overlap for better link prediction in multiplex networks, addressing incomplete observed connections.

Findings

ML-BNMTF outperforms baseline methods in low-overlap conditions.

Community co-membership across similar layers increases link prediction accuracy.

Inter-layer community overlap is a key factor in multiplex link prediction.

Abstract

Multiplex networks are a representation of real-world complex systems as a set of entities (i.e. nodes) connected via different types of connections (i.e. layers). The observed connections in these networks may not be complete and the link prediction task is about locating the missing links across layers. Here, the main challenge is about collecting relevant evidence from different layers to assist the link prediction task. It is known that co-membership in communities increases the likelihood of connectivity between nodes. We discuss that co-membership in the communities of the similar layers augments the chance of connectivity. The layers are considered similar if they show significant inter-layer community overlap. Moreover, we found that although the presence of link is correlated in layers but the extent of this correlation is not the same across different communities. Our proposed, ML-BNMTF, as a link prediction method in multiplex networks, is devised based on these findings. ML-BNMTF outperforms baseline methods specifically when the global link overlap is low.