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- W4286377478 abstract "Today's social networks present multiplex and heterogeneous attributes, and they are becoming more and more difficult to grasp the unique attributes and structures of the network from the whole network structures. The attribute factors such as Degree Centrality and Closeness Centrality are inadequate in the study of social networks if they are not modified and taken fully into account the real characteristics. Although the evolution of a social network is a macroscopic process, the state of each node at different moments determines the trend of the overall state on the network. In order to better identify the key nodes in the social network and more really simulate information diffusion process, this paper proposes the method using WIE (Weighted Information Entropy) to measure node's influent force and importance degree based on the concept of information entropy in information theory, which carries higher-order local information of each node rather than only contains first-order local information like degree does. Different from the conventional SIR model, this paper realizes the dynamic calculation of the diffusion probability β in the improved SIR model according to node's local influence and weighted value. Finally, four kinds of real social networks are selected and analyzed. The experiment shows that the WIE measurement and the dynamically calculated diffusion probability can more truly reflect the information transmission in real social networks, and the node sets selected by WIE values have at least 10 percent improvement in diffusion efficiency." @default.
- W4286377478 created "2022-07-21" @default.
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- W4286377478 date "2022-01-01" @default.
- W4286377478 modified "2023-09-28" @default.
- W4286377478 title "Dynamic Information Diffusion Model Based on Weighted Information Entropy" @default.
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- W4286377478 doi "https://doi.org/10.1007/978-981-19-4549-6_39" @default.
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