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Social Network Analysis and Mining

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01.12.2022 | Original Article

Machine learning-based method to predict influential nodes in dynamic social networks

verfasst von: Wafa Karoui, Nesrine Hafiene, Lotfi Ben Romdhane

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2022

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Abstract

A challenging issue in complex networks is to effectively predict a set of influential nodes. Most previous studies typically ignore the local updates effects on the diffusion process and predict only interactions between nodes. For a good prediction of the influential nodes, each node structure and semantic features can help. In this paper, we propose a novel approach DPIN, a machine learning-based approach, to predict future influential nodes taking into account the structural and semantic characteristics of nodes. We apply this method to predict “hot” papers. Semantic features correspond to “hot” topics detected by the LDA model and structure features correspond to bibliometric features. This approach can help authors make good choices about which topic to target, which article to read, which article to cite and which collaboration to favor. Finally, DPIN experimentations on the DBLP dynamic social network confirm the high quality of predictions.

Vorheriger Artikel Longitudinal change in language behaviour during protests: a case study of Euromaidan in Ukraine

Nächster Artikel Bibliometric analysis of the published literature on machine learning in economics and econometrics

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Titel: Machine learning-based method to predict influential nodes in dynamic social networks
verfasst von: Wafa Karoui
Nesrine Hafiene
Lotfi Ben Romdhane
Publikationsdatum: 01.12.2022
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2022
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-022-00942-4

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