Predicting information diffusion on social networks with partial knowledge

Anis Najar 1 Ludovic Denoyer 1 Patrick Gallinari 1
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Models of information diffusion and propagation over large social media usually rely on a Close World Assumption: information can only propagate onto the network relational structure, it cannot come from external sources, the network structure is supposed fully known by the model. These assumptions are nonrealistic for many propagation processes extracted from Social Websites. We address the problem of predicting information propagation when the network diffusion structure is unknown and without making any closed world assumption. Instead of modeling a diffusion process, we propose to directly predict the final propagation state of the information over a whole user set. We describe a general model, able to learn predicting which users are the most likely to be contaminated by the information knowing an initial state of the network. Different instances are proposed and evaluated on artificial datasets.
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Contributor : Ludovic Denoyer <>
Submitted on : Tuesday, August 30, 2016 - 10:17:12 AM
Last modification on : Thursday, March 21, 2019 - 2:19:02 PM



Anis Najar, Ludovic Denoyer, Patrick Gallinari. Predicting information diffusion on social networks with partial knowledge. WWW'12 - The 21st International Conference on World Wide Web, Apr 2012, Lyon, France. pp.1197-1204, ⟨10.1145/2187980.2188261⟩. ⟨hal-01357568⟩



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