LayerPlexRank: Exploring Node Centrality and Layer Influence through Algebraic Connectivity in Multiplex Networks
<|reference_start|>LayerPlexRank: Exploring Node Centrality and Layer Influence through Algebraic Connectivity in Multiplex Networks: 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.<|reference_end|>
@article{ren2024layerplexrank:,
    title={LayerPlexRank: Exploring Node Centrality and Layer Influence through
  Algebraic Connectivity in Multiplex Networks},
    author={Hao Ren and Jiaojiao Jiang},
    journal={arXiv preprint arXiv:2405.05576},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05576},
    primaryClass={cs.SI cs.IR cs.NI}
}