A review of the research on identifying influential nodes in complex networks considering cascading effects: Theory and engineering applications

Abstract

The functional robustness and structural integrity of real-world networks disproportionately on a subset of influential nodes. Identifying these influential nodes is a central objective in network science, underpinning applications spanning from epidemic control to predicting high-impact journal publications. In this study, cascade mitigation is defined as a pivotal, yet overlooked, canonical problem in network science that has received comparatively limited attention, and a generalized benchmarking framework is established to unify this interdisciplinary field. A decade of progress is synthesized, tracing the evolution from classical structure-centrality-based to artificial intelligence (AI)-based influential-node identification techniques. Beyond providing a systematic survey, nearly 20 distinct methodologies were empirically evaluated across diverse synthetic and real-world data sets using standard spreading models such as the susceptible-infected-recovered, linear threshold, and independent cascade models. This large-scale cross-domain comparison elucidates the performance differences between structural and AI-based strategies. By standardising the evaluation of node influence, this review offers a critical roadmap for researchers and identifies promising avenues for future interdisciplinary innovations.

Graphical abstract

Keywords

complex network / identification of influential nodes / cascading effect

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