Influential Nodes Identification for Complex Networks Based on Multi-Feature Fusion

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Abstract
In the realm of complex network analysis, identifying crucial nodes represents a formidable challenge that has attracted considerable research focus. Historically, research efforts often neglected the significance of incorporating spatial node information, thereby constraining the accurate identification of key nodes. Addressing this gap, this study introduces an advanced centrality model named Degree-K-shell-Betweenness centrality (DKBC), leveraging gravitational principles. The DKBC model integrates node degree, spatial positioning, and betweenness centrality attributes to enhance the precision of crucial node identification for complex networks. This innovative approach surpasses conventional gravity-based methodologies in efficacy. Evaluation of the proposed algorithm employs the Susceptible-Infectious-Recovered (SIR) Epidemic model and assesses correlation using the Kendall coefficient τ. Comparative analysis against a standard benchmark algorithm underscores the superior performance of the DKBC model. Empirical validation conducted across twelve real-world networks substantiates the exceptional accuracy of DKBC model in pinpointing critical nodes. This research significantly advances the field by demonstrating the efficacy of integrating spatial information into centrality metrics for enhanced network analysis and application.

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