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

Simon Bourigault 1 Sylvain Lamprier 1 Patrick Gallinari 1
1 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
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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Communication dans un congrès
International Conference on Web Search and Data Mining, Feb 2016, San Francisco, United States. ACM, pp.573-582, 〈10.1145/2835776.2835817〉
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https://hal.archives-ouvertes.fr/hal-01316795
Contributeur : Simon Bourigault <>
Soumis le : mardi 17 mai 2016 - 16:45:12
Dernière modification le : lundi 26 novembre 2018 - 01:21:33

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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. ACM, pp.573-582, 〈10.1145/2835776.2835817〉. 〈hal-01316795〉

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