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

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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Conference papers
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https://hal.archives-ouvertes.fr/hal-01345751
Contributor : Simon Bourigault <>
Submitted on : Friday, July 15, 2016 - 3:34:52 PM
Last modification on : Thursday, March 21, 2019 - 1:09:21 PM

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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. pp.265-281, ⟨10.1007/978-3-319-46227-1_17⟩. ⟨hal-01345751⟩

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