Learning Distributed Representations of Users for Source Detection in Online Social Networks

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 study the problem of source detection in the contextof information diffusion through online social networks. We propose a representationlearning approach that leads to a robust model able to deal with the sparsityof the data. From learned continuous projections of the users, our approachis able to efficiently predict the source of any newly observed diffusion episode.Our model does not rely neither on a known diffusion graph nor on a hypotheticalprobabilistic diffusion law, but directly infers the source from diffusion episodes.It is also less complex than alternative state of the art models. It showed goodperformances on artificial and real-world datasets, compared with various stateof the art baselines.
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Communication dans un congrès
ECML PKDD 2016 - European Conference on Machine Learning and Knowledge Discovery in Databases, Sep 2016, Riva del Garda, Italy. Springer, 9852, pp.265-281, Lecture Notes in Computer Science. 〈10.1007/978-3-319-46227-1_17〉
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https://hal.archives-ouvertes.fr/hal-01345751
Contributeur : Simon Bourigault <>
Soumis le : vendredi 15 juillet 2016 - 15:34:52
Dernière modification le : jeudi 22 novembre 2018 - 14:30:27

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Simon Bourigault, Sylvain Lamprier, Patrick Gallinari. Learning Distributed Representations of Users for Source Detection in Online Social Networks. ECML PKDD 2016 - European Conference on Machine Learning and Knowledge Discovery in Databases, Sep 2016, Riva del Garda, Italy. Springer, 9852, pp.265-281, Lecture Notes in Computer Science. 〈10.1007/978-3-319-46227-1_17〉. 〈hal-01345751〉

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