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- W3206559783 abstract "AbstractOnline social networks contain a lot of information about people, their interests, activities, behaviour patterns, etc., and can be efficient to find the most influential people based on various trades. Studying these networks can help solve a variety of purposes such as rumour prevention, controlled information flow, finding target audience for products and movies, etc. This can help to spread/prevent the flow of information to people very quickly by identifying the most critical or the most influential nodes in the network, from where the information spread is maximum. This paper focuses on creating a hybrid centrality and formulating an algorithm to find these nodes with the most collective influence.KeywordsHybrid node centralitySIR (Susceptible-Infected-Recovered) modelSpreading influence maximization" @default.
- W3206559783 created "2021-10-25" @default.
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- W3206559783 date "2021-10-19" @default.
- W3206559783 modified "2023-09-24" @default.
- W3206559783 title "Identifying K-Most Influential Nodes in a Social Network Using K-Hybrid Method of Ranking" @default.
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- W3206559783 doi "https://doi.org/10.1007/978-981-16-4641-6_17" @default.
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