165111 20251212165958.0 doi 10.1016/j.chaos.2025.117549 sideral 146645 ART-2025-146645 eng Zheng, Ru A new algorithm for identifying influential nodes in multiplex networks 2025 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. info:eu-repo/grantAgreement/ES/DGA/E36-23R info:eu-repo/grantAgreement/ES/MICINN/PID2023-149409NB-I00 info:eu-repo/semantics/closedAccess All rights reserved http://www.europeana.eu/rights/rr-f/ info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Guo, Yaoqi Moreno, Yamir Universidad de Zaragoza (orcid)0000-0002-0895-1893 2004 405 Universidad de Zaragoza Dpto. Física Teórica Área Física Teórica 202, Part 2 (2025), 117549 [12 pp.] Chaos, solitons fractals Chaos, Solitons and Fractals 0960-0779 1182254 http://zaguan.unizar.es/record/165111/files/texto_completo.pdf Versión publicada 2246198 http://zaguan.unizar.es/record/165111/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:165111 articulos driver 2025-12-12-14:42:53 ARTICLE