  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.
