Representation Learning for Information Diffusion through Social Networks: an Embedded Cascade Model.
WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining San Francisco California USA February, 2016(2016)
摘要
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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关键词
Machine learning, Information diffusion, Representation Learning
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