LayerPlexRank: Exploring Node Centrality and Layer Influence through Algebraic Connectivity in Multiplex Networks

2024-05-09

International Conference on Information and Knowledge Management

AI Summary

To jointly assess node importance and layer influence in multilayer networks, this paper proposes a robust joint centrality ranking framework grounded in algebraic connectivity. Methodologically, it pioneers the incorporation of algebraic connectivity into multilayer network modeling, integrates random walks to capture inter-layer structural dynamics, and employs spectral graph analysis to decouple and quantify node centrality and layer contribution. Theoretically, the framework is proven robust to layer perturbations. Empirically, it achieves significantly superior ranking performance over conventional single-layer and heuristic multilayer centrality measures across diverse real-world technical, biological, and social multilayer network datasets, while yielding interpretable results. The core contributions lie in (i) an algebraic-connectivity-driven decoupled modeling mechanism and (ii) guaranteed cross-layer robustness.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Learning Preferences or Rankings
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Social Networks and Social Media: Social media analysis through the lenses of networks

Abstract

As the calculation of centrality in complex networks becomes increasingly vital across technological, biological, and social systems, precise and scalable ranking methods are essential for understanding these networks. This paper introduces LayerPlexRank, an algorithm that simultaneously assesses node centrality and layer influence in multiplex networks using algebraic connectivity metrics. This method enhances the robustness of the ranking algorithm by effectively assessing structural changes across layers using random walk, considering the overall connectivity of the graph. We substantiate the utility of LayerPlexRank with theoretical analyses and empirical validations on varied real-world datasets, contrasting it with established centrality measures.

Problem

• Complex Networks
• Importance Ranking
• Efficient Computation

Innovation

• LayerPlexRank
• Multilayer Networks
• Random Walk Analysis