Influential node identification via network embedding and structural feature fusion

Heping Zhang, Li Dong, Weida Xu, Yang Wu, Xiaoyi Jin, Xiaoyao Xie and Hegui Zhang

Chaos, Solitons & Fractals, 2026, vol. 208, issue P1

Abstract: Influential node identification in complex networks remains challenging due to limited generalizability and high computational costs of existing methods. To address these issues, we reformulate the task as a regression problem and propose CNINI (Complex Network Influential Node Identification), a framework that integrates network embedding with local centrality measures to capture both global topological structures and local influence patterns. CNINI employs network embedding techniques to learn node representations, which are combined with centrality measures to construct more informative features. In a supervised setting, the SIR model is used to simulate influence propagation and generate training labels. The regression model is trained on multiple synthetic networks and then applied to unseen networks, reducing the need for costly iterative propagation simulations during inference. Extensive experiments on eight real-world datasets demonstrate that CNINI surpasses 11 state-of-the-art baseline methods, including centrality- and embedding-based methods.

Keywords: Complex networks; Influential nodes; Network embedding; Regression model (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:208:y:2026:i:p1:s096007792600233x

DOI: 10.1016/j.chaos.2026.118092

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