Abstract
The widespread use of online social network (OSN) and the often-increasing volume of information provided by their users have motivated both corporate and scientific researchers to investigate how certain systems can be manipulated. According to recent findings, monitoring and evaluating the influence of OSN users has significant applications in the fields of health, economics, education, politics, entertainment, and other fields. The propagation model has an impact on a centrality measure’s capacity to show a node’s ability to disseminate influence. However, certain modeling techniques, a centrality measure that performs well on wandering and un-weightiness networks may produce low performance. To improve prediction performance, new-centrality measures and combined centrality measures are proposed employing linear combinations of centrality metrics.
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Jothi, P., Padmapriya, R. (2023). An Influential User Prediction in Social Network Using Centrality Measures and Deep Learning Method. In: Khanna, A., Polkowski, Z., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes in Networks and Systems, vol 572. Springer, Singapore. https://doi.org/10.1007/978-981-19-7615-5_66
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DOI: https://doi.org/10.1007/978-981-19-7615-5_66
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