PPEIM: A preference path-based early-stage influence accumulation model for influential nodes identification in locally dense multi-core networks

• Published in: Journal of computational science 2025-02, Vol.85, p.102479, Article 102479
• Main Authors: Zhang, Yaofang, Wang, Zibo, Liu, Yang, Zhao, Ruohan, Liu, Hongri, Wang, Bailing
• Format: Article
• Language: English
• Subjects: Early-stage influence; Gravity model; Influential nodes; Locally dense multi-core network; Random walk
• ISSN: 1877-7503
• DOI: 10.1016/j.jocs.2024.102479

The identification of influential nodes, a critical problem in the field of complex networks, has been extensively studied. However, previous research has primarily focused on maximizing terminal influence across all network structures indiscriminately, making it challenging to accurately identify influential nodes in specific structures. Moreover, overlooking the influence of the temporal dynamics of propagation significantly diminishes the benefits of identifying influential nodes. Therefore, we propose an influential nodes identification model, Preference Path-based Early-stage Influence Accumulation Model (PPEIM), tailored for typical locally dense multi-core networks. The key idea of PPEIM is to identify more influential nodes in early-stage propagation by aggregating dynamic influence propagation volumes superimposed on multiple paths. Specifically, early-stage influence performance is enhanced by sampling paths, mitigating the risk of dense influential nodes resulting from redundant relationships. Moreover, the K-shell, degree, influence distance and link direction are integrated to define connection strength between nodes to guide path selection. And the concept of influence propagation volume is introduced to accurately simulate the influence residuals and losses during the propagation process. To validate the effectiveness and superiority of PPEIM in locally dense multi-core networks, five sets of simulation experiments are conducted on seven real-world datasets. Experimental results demonstrate that PPEIM outperforms six state-of-the-art methods in overall propagation capability, early-stage influence capability, disintegration capability, node dispersion, and discrimination capability. •A model aims to identify influential nodes in locally dense multi-core networks.•Positional and neighborhood information are combined to optimize the gravity model.•The optimized gravity model is used to guide random walk probability assignment.•The influence propagation volume reflects how nodes carry and propagate influence.•The proposed model outperforms than the comparison methods in multiple aspects.