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Existing information diffusion models usually model the diffusion process based on the underlying networks, while the diffusion networks in real world are more complex than that of the underlying networks. In this paper, we propose a matrix factorization based predictive model (MFPM) to directly model the diffusion process we had observed and predict the information diffusion states in the future...
Data of information cascade in social network is always incomplete with missing information of the links between nodes. This paper proposes a generative probabilistic model to infer links using the observation data. Comparing to existing methods, we take consideration of differences of links. And we are also in view of recurrent events and influence from outside of the cascade. Our hawkes process...
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