Link Prediction in Multiplex Networks based on Interlayer Similarity

TL;DR

This paper introduces a new link prediction framework for multiplex networks that leverages interlayer similarity and proximity features, demonstrating superior performance over existing methods on synthetic and real data.

Contribution

It presents a novel, learning-free approach that utilizes interlayer similarity and proximity features for link prediction in multiplex networks.

Findings

Outperforms state-of-the-art algorithms in experiments

Effective on both synthetic and real-world multiplex networks

Simple to compute without requiring learning algorithms

Abstract

Some networked systems can be better modelled by multilayer structure where the individual nodes develop relationships in multiple layers. Multilayer networks with similar nodes across layers are also known as multiplex networks. This manuscript proposes a novel framework for predicting forthcoming or missing links in multiplex networks. The link prediction problem in multiplex networks is how to predict links in one of the layers, taking into account the structural information of other layers. The proposed link prediction framework is based on interlayer similarity and proximity-based features extracted from the layer for which the link prediction is considered. To this end, commonly used proximity-based features such as Adamic-Adar and Jaccard Coefficient are considered. These features that have been originally proposed to predict missing links in monolayer networks, do not require learning, and thus are simple to compute. The proposed method introduces a systematic approach to take into account interlayer similarity for the link prediction purpose. Experimental results on both synthetic and real multiplex networks reveal the effectiveness of the proposed method and show its superior performance than state-of-the-art algorithms proposed for the link prediction problem in multiplex networks.