Abstract
The identification of spreading influence nodes in social networks, which studies how to detect important individuals in human society, has attracted increasing attention from physical and computer science, social science and economics communities. The identification algorithms of spreading influence nodes can be used to evaluate the spreading influence, describe the node’s position, and identify interaction centralities. This review summarizes the recent progress about the identification algorithms of spreading influence nodes from the viewpoint of social networks, emphasizing the contributions from physical perspectives and approaches, including the microstructure-based algorithms, community structure-based algorithms, macrostructure-based algorithms, and machine learning-based algorithms. We introduce diffusion models and performance evaluation metrics, and outline future challenges of the identification of spreading influence nodes.
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This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 72171150, 71771152, 61773248, and 71901144), and the Major Program of National Fund of Philosophy and Social Science of China (Grant Nos. 18ZDA088 and 20ZDA060).
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Ou, Y., Guo, Q. & Liu, J. Identifying spreading influence nodes for social networks. Front. Eng. Manag. 9, 520–549 (2022). https://doi.org/10.1007/s42524-022-0190-8
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DOI: https://doi.org/10.1007/s42524-022-0190-8