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A new algorithm for identifying influential nodes in multiplex networks

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  • Zheng, Ru
  • Guo, Yaoqi
  • Moreno, Yamir

Abstract

Multiplex networks provide a powerful conceptual framework for analyzing complex systems characterized by multi-dimensional interdependencies. Moreover, the dynamics on top of these networks could describe processes such as epidemic spreading and the resilience of real interconnected systems more accurately. Identifying influential nodes in such multilayered systems remains a crucial challenge. Here, we propose a novel model, the multiplex multi-attribute Laplacian gravity model (MMALG), to assess the influence of nodes in multiplex networks based on the Laplacian centrality and the gravity model. Experimental analyses on both synthetic and real-world networks reveal that the proposed method outperforms others in discovering key nodes when disease dynamics are simulated on top of multiplex networks.

Suggested Citation

  • Zheng, Ru & Guo, Yaoqi & Moreno, Yamir, 2026. "A new algorithm for identifying influential nodes in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 202(P2).
    • Handle: RePEc:eee:chsofr:v:202:y:2026:i:p2:s0960077925015620
    DOI: 10.1016/j.chaos.2025.117549
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