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• W4386204057 abstract "Abstract We propose a new deep learning cascade prediction model CasSIM that can simultaneously achieve two most demanded objectives: popularity prediction and final adopter prediction . Compared to existing methods based on cascade representation, CasSIM simulates information diffusion processes by exploring users’ dual roles in information propagation with three basic factors: users’ susceptibilities , influences and message contents . With effective user profiling, we are the first to capture the topic-specific property of susceptibilities and influences. In addition, the use of graph neural networks allows CasSIM to capture the dynamics of susceptibilities and influences during information diffusion. We evaluate the effectiveness of CasSIM on three real-life datasets and the results show that CasSIM outperforms the state-of-the-art methods in popularity and final adopter prediction." @default.
• W4386204057 created "2023-08-28" @default.
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• W4386204057 date "2023-08-27" @default.
• W4386204057 modified "2023-10-09" @default.
• W4386204057 title "A tale of two roles: exploring topic-specific susceptibility and influence in cascade prediction" @default.
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• W4386204057 doi "https://doi.org/10.1007/s10618-023-00953-5" @default.
• W4386204057 hasPublicationYear "2023" @default.
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