Representation Learning for Information Diffusion through Social Networks: an Embedded Cascade Model

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.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01316795
Contributor : Simon Bourigault <>
Submitted on : Tuesday, May 17, 2016 - 4:45:12 PM
Last modification on : Thursday, March 21, 2019 - 2:30:11 PM

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Simon Bourigault, Sylvain Lamprier, Patrick Gallinari. Representation Learning for Information Diffusion through Social Networks: an Embedded Cascade Model. International Conference on Web Search and Data Mining, Feb 2016, San Francisco, United States. pp.573-582, ⟨10.1145/2835776.2835817⟩. ⟨hal-01316795⟩

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