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• W3112846528 abstract "The growing popularity of Online Social Networks (OSN) have prompted an increasing number of companies to promote their brands and products through social media. This paper presents a topological potential scheme for predicting influential nodes from large scale OSNs to support more intelligent brand communication. We first construct a weighted network model for the users and their relationships extracted from the brand-related content in OSNs. We quantitatively measure the individual value of the nodes from both the network structure and brand engagement aspects. Moreover, we have addressed the problem of influence decay along with information propagation in social networks and use the topological potential theory to evaluate the importance of the nodes by their individual values as well as the individual values of their surrounding nodes. The experimental results have shown that the proposed method is able to predict influential nodes in large-scale OSNs. We investigate the top-k influential nodes identified by our method in detail, which are quite different from those identified by using pure network structure or individual value. We can obtain an identification result with a higher ratio of verified users and user coverage by using our method compared to existing typical approaches." @default.
• W3112846528 created "2020-12-21" @default.
• W3112846528 creator A5036951998 @default.
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• W3112846528 date "2021-01-01" @default.
• W3112846528 modified "2023-10-16" @default.
• W3112846528 title "TPS: A Topological Potential Scheme to Predict Influential Network Nodes for Intelligent Communication in Social Networks" @default.
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• W3112846528 doi "https://doi.org/10.1109/tnse.2020.3044299" @default.
• W3112846528 hasPublicationYear "2021" @default.
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