Assessing the Robustness and Reducibility of Multiplex Networks with Embedding-Aided Interlayer Similarities

TL;DR

This paper introduces EATSim, a novel method for measuring interlayer similarity in multiplex networks that improves accuracy by combining structural and anchor node alignment information, with applications in robustness and reducibility analysis.

Contribution

The paper proposes EATSim, a new comprehensive approach for interlayer similarity measurement that outperforms existing methods and enhances analysis of complex interconnected systems.

Findings

EATSim accurately captures geometric similarities in networks.

EATSim outperforms existing methods in similarity measurement.

EATSim improves predictions of network robustness and reducibility.

Abstract

The study of interlayer similarity of multiplex networks helps to understand the intrinsic structure of complex systems, revealing how changes in one layer can propagate and affect others, thus enabling broad implications for transportation, social, and biological systems. Existing algorithms that measure similarity between network layers typically encode only partial information, which limits their effectiveness in capturing the full complexity inherent in multiplex networks. To address this limitation, we propose a novel interlayer similarity measuring approach named Embedding Aided inTerlayer Similarity (EATSim). EATSim concurrently incorporates intralayer structural similarity and cross-layer anchor node alignment consistency, providing a more comprehensive framework for analyzing interconnected systems. Extensive experiments on both synthetic and real-world networks demonstrate that EATSim effectively captures the underlying geometric similarities between interconnected networks, significantly improving the accuracy of interlayer similarity measurement. Moreover, EATSim achieves state-of-the-art performance in two downstream applications: predicting network robustness and network reducibility, showing its great potential in enhancing the understanding and management of complex systems.