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.

翻译：随着复杂网络中中心性计算在技术、生物和社会系统中变得日益重要，精确且可扩展的排序方法对于理解这些网络至关重要。本文提出LayerPlexRank算法，该算法使用代数连通性度量同时评估多重网络中的节点中心性与层影响力。该方法通过随机游走有效评估各层的结构变化，并考虑图的整体连通性，从而增强了排序算法的鲁棒性。我们通过理论分析及在多种真实世界数据集上的实证验证，将LayerPlexRank与已有中心性度量进行对比，证实了其实用性。