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- W3045731975 abstract "Recently, online social networks are playing an ever-important role in both our social life and economy. Therefore, modeling of information diffusion over social networks is a common research topic, which is of crucial importance to better understand how the avalanche of information overflow leads to the detrimental consequences, and how to motivate some beneficial information spreading. However, most model-based works on information diffusion either merely consider the spreading of one single message or make the assumption that different diffusion processes are independent of each other. In real-world scenarios, multi-source correlated information often spread together, which jointly influence users’ decisions. In this chapter, we aim to model the multi-source information diffusion processes from a graphical evolutionary game perspective. Specifically, we model users’ local interactions and strategic decision making process, and analyze the evolutionary dynamics of the diffusion processes of correlated information. We conduct simulations on both synthetic and Facebook real-world networks, and the simulation results are consistent with our theoretical analysis. We also test the model on the users’ forwarding data in “Weibo” social networks and observe an effective prediction performance on the real-world information spreading processes." @default.
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- W3045731975 date "2020-01-01" @default.
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- W3045731975 title "Diffusion of Multi-source Correlated Information" @default.
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- W3045731975 doi "https://doi.org/10.1007/978-981-15-7160-2_5" @default.
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