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• W2296752489 abstract "In this paper, we focus on information diffusion through social networks. Based on the well-known Independent Cascade model, we embed users of the social network in a latent space to extract more robust diffusion probabilities than those defined by classical graphical learning approaches. Better generalization abilities provided by the use of such a projection space allows our approach to present good performances on various real-world datasets, for both diffusion prediction and influence relationships inference tasks. Additionally, the use of a projection space enables our model to deal with larger social networks." @default.
• W2296752489 created "2016-06-24" @default.
• W2296752489 creator A5027436363 @default.
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• W2296752489 date "2016-02-08" @default.
• W2296752489 modified "2023-09-28" @default.
• W2296752489 title "Representation Learning for Information Diffusion through Social Networks" @default.
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• W2296752489 doi "https://doi.org/10.1145/2835776.2835817" @default.
• W2296752489 hasPublicationYear "2016" @default.
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