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

TL;DR

LayerPlexRank is a novel algorithm that evaluates node importance and layer influence in multiplex networks by leveraging algebraic connectivity, providing a scalable and robust ranking method validated through theoretical and empirical analysis.

Contribution

It introduces LayerPlexRank, a new method combining algebraic connectivity with random walk techniques to assess centrality and layer influence in multiplex networks.

Findings

Outperforms traditional centrality measures in robustness.

Effectively captures structural changes across network layers.

Validated on diverse real-world datasets.

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.