Investigating Negative Interactions in Multiplex Networks: A Mutual Information Approach

TL;DR

This paper presents a mutual information-based method called SMLP for predicting both positive and negative links in multiplex networks, improving understanding of complex layered interactions.

Contribution

It introduces a novel approach leveraging negative relationship layers to enhance link prediction in multiplex networks.

Findings

SMLP outperforms existing methods in link prediction accuracy.

Negative layers provide valuable information for predicting positive links.

The approach effectively captures complex inter-layer dependencies.

Abstract

Many interesting real-world systems are represented as complex networks with multiple types of interactions and complicated dependency structures between layers. These interactions can be encoded as having a valence with positive links marking interactions such as trust and friendship and negative links denoting distrust or hostility. Extracting information from these negative interactions is challenging since standard topological metrics are often poor predictors of negative link formation, particularly across network layers. In this paper, we introduce a method based on mutual information which enables us to predict both negative and positive relationships. Our experiments show that SMLP (Signed Multiplex Link Prediction) can leverage negative relationship layers in multiplex networks to improve link prediction performance.