A Holistic Approach for Predicting Links in Coevolving Multilayer Networks

TL;DR

This paper presents MLP, a holistic framework for predicting links in coevolving multilayer networks by leveraging cross-layer information to improve prediction accuracy over traditional methods.

Contribution

Introduces a novel multilayer link prediction framework that combines cross-layer information to enhance link prediction in dynamic multiplex networks.

Findings

Reweighting based on cross-layer likelihoods improves prediction accuracy.

The proposed method outperforms existing fusion techniques.

Holistic approach captures inter-layer dependencies effectively.

Abstract

Networks extracted from social media platforms frequently include multiple types of links that dynamically change over time; these links can be used to represent dyadic interactions such as economic transactions, communications, and shared activities. Organizing this data into a dynamic multiplex network, where each layer is composed of a single edge type linking the same underlying vertices, can reveal interesting cross-layer interaction patterns. In coevolving networks, links in one layer result in an increased probability of other types of links forming between the same node pair. Hence we believe that a holistic approach in which all the layers are simultaneously considered can outperform a factored approach in which link prediction is performed separately in each layer. This paper introduces a comprehensive framework, MLP (Multilayer Link Prediction), in which link existence likelihoods for the target layer are learned from the other network layers. These likelihoods are used to reweight the output of a single layer link prediction method that uses rank aggregation to combine a set of topological metrics. Our experiments show that our reweighting procedure outperforms other methods for fusing information across network layers.