What does this research mean for the field?

The field of influential node identification has evolved from structural centrality-based methods to machine-learning-based methods, with future progress depending on hybrid approaches, improved interpretability, and evaluation in dynamic and large-scale networks. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To review, categorize, and benchmark methods for identifying influential nodes in complex networks, comparing classical structural centrality metrics against modern machine learning techniques.

September 3, 2026ACM Computing Surveys

A Comprehensive Survey on Identifying Influential Nodes: From Structural Centrality-based to Learning-based Methods

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To review, categorize, and benchmark methods for identifying influential nodes in complex networks, comparing classical structural centrality metrics against modern machine learning techniques.
Reviewed foundational problem formulations and state-of-the-art algorithms across structural centrality and machine-learning paradigms.
Developed a multidimensional evaluation framework analyzing algorithmic scalability, interpretability, computational complexity, and application contexts.
Surveyed standardized performance evaluation metrics and explored emerging directions, including dynamic network analysis and large language model applications.
Categorized existing node influence identification approaches into structural centrality-based heuristics and predictive machine-learning frameworks.
Identified critical computational trade-offs, demonstrating that machine-learning approaches enhance predictive capability at the cost of interpretability and scalability relative to structural measures.
Characterized key future research priorities, emphasizing hybrid identification architectures, enhanced model explainability, and evaluation in dynamic, large-scale networks.

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Implication

Comprehensive survey categorizes influential node detection methods in complex networks, highlighting trade-offs between structural centrality and machine learning.

Cite This Study

Sheikhahmadi et al. (2026) studied this question.

synapsesocial.com/papers/6a993511636c6408cfa7cf02 https://doi.org/10.1145/3844941

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Implication

Comprehensive survey categorizes influential node detection methods in complex networks, highlighting trade-offs between structural centrality and machine learning.

A Comprehensive Survey on Identifying Influential Nodes: From Structural Centrality-based to Learning-based Methods

Key Points

Discussion

Implication

Cite This Study

A Comprehensive Survey on Identifying Influential Nodes: From Structural Centrality-based to Learning-based Methods

Key Points

Discussion

Implication

Cite This Study

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