Abstract
In any online social media platform, it is necessary to reduce the effect of rumor data from original information as it may cause harm to society. Influential users can be detected through different centrality measures. When the rumor is generated through some influential users, they have more impact on society. Here, we have proposed a prognostic method to distinguish those influential users of online social media, based on network analysis. The susceptible-infectious-recovered (SIR) model has been used for simulation of the propagation of information. For the particular seed nodes which have been chosen by practicing different centrality measures, detailed relative learning in terms of infected nodes is also exhibited. Selection of seed nodes through centrality measure is computationally exhaustive; therefore, we form a composite model, where the original social network is decomposed using the k-core, and centrality nodes are obtained from that decomposed network. Centrality measurements thus derived from the generated network are used as the seed of information propagation. Another important result derived from the empirical study is that not only the influential nodes, but neighbors of the influential nodes also have a greater impact on maximizing the effect of rumors.
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Ganguly, M., Dey, P., Chatterjee, S., Roy, S. (2023). Influential Node Detection in Online Social Network for Influence Minimization of Rumor. In: Basu, S., Kole, D.K., Maji, A.K., Plewczynski, D., Bhattacharjee, D. (eds) Proceedings of International Conference on Frontiers in Computing and Systems. Lecture Notes in Networks and Systems, vol 404. Springer, Singapore. https://doi.org/10.1007/978-981-19-0105-8_58
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DOI: https://doi.org/10.1007/978-981-19-0105-8_58
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