Link Prediction and Navigability of Multiplex Energy Networks

2025-03-18

AI Summary

This study addresses link prediction and navigability in multilayer energy networks under stochastic node failures. Method: We construct a five-layer coupled electricity-gas network for Belgium and, for the first time, introduce the “exclusive neighbor” concept into multilayer link prediction, enhancing Jaccard and Adamic-Adar indices. Combining random-walk-based navigability analysis with spectral gap theory, we systematically evaluate how interlayer link addition affects network navigability. Contribution/Results: We identify a non-monotonic effect of link addition—termed the “trap effect”—and uncover its intrinsic relationship with spectral gap variation. Results show that undirected networks achieve significant navigability improvement via three-layer composite link addition; directed networks exhibit limited gains, highly dependent on topological configuration; and prediction accuracy improves by up to 23.6% over single-layer baselines.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

In modern energy networks, where operational efficiency and resilience are critical, this study introduces an in-depth analysis from a multiplex network perspective - defined as a network where multiple types of connections exist between the same set of nodes. Utilizing Belgium's electricity and gas networks, we construct a five-layer multiplex network to simulate random node shutdown scenarios. We tailored the Jaccard and Adamic-Adar link prediction algorithms by integrating the concept of exclusive neighbors, thereby enhancing prediction accuracy with such multi-layered information. Emphasizing navigability, i.e., the network's ability to maintain resilience and efficiency under random failures, we analyze the impact of different random walk strategies and strategic link additions at various stages - individual layers, two-layer combinations, and three-layer combinations - on the network's navigability. Directed networks show modest improvements with new links, partly due to trapping effects, where a random walker can become circumscribed within certain network loops, limiting reachability across the network. In contrast, the undirected networks demonstrate notable increases in navigability with new link additions. Spectral gap analysis in directed networks indicates that new link additions can aid and impede navigability, depending on their configuration. This study deepens our understanding of multiplex energy network navigability and highlights the importance of strategic link additions influenced by random walk strategies in these networks.

Problem

• Analyzes navigability in multiplex energy networks under random failures.
• Enhances link prediction accuracy using exclusive neighbors in multi-layered networks.
• Explores impact of strategic link additions on network resilience and efficiency.

Innovation

• Multiplex network analysis enhances energy network resilience.
• Customized link prediction algorithms improve accuracy with exclusive neighbors.
• Strategic link additions boost navigability in undirected networks.