Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning a kernel matrix for nonlinear dimensionality reduction
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Prediction of Information Diffusion Probabilities for Independent Cascade Model
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Mining social networks using heat diffusion processes for marketing candidates selection
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Predicting the popularity of online content
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Inferring networks of diffusion and influence
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Modeling Information Diffusion in Implicit Networks
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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Learning diffusion probability based on node attributes in social networks
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
A predictive model for the temporal dynamics of information diffusion in online social networks
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Predicting information diffusion on social networks with partial knowledge
Proceedings of the 21st international conference companion on World Wide Web
Feature-Enhanced probabilistic models for diffusion network inference
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Proceedings of the sixth ACM international conference on Web search and data mining
Predicting information diffusion in social networks using content and user's profiles
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
On the precision of social and information networks
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Analyzing and modeling the temporal diffusion of information on social media has mainly been treated as a diffusion process on known graphs or proximity structures. The underlying phenomenon results however from the interactions of several actors and media and is more complex than what these models can account for and cannot be explained using such limiting assumptions. We introduce here a new approach to this problem whose goal is to learn a mapping of the observed temporal dynamic onto a continuous space. Nodes participating to diffusion cascades are projected in a latent representation space in such a way that information diffusion can be modeled efficiently using a heat diffusion process. This amounts to learning a diffusion kernel for which the proximity of nodes in the projection space reflects the proximity of their infection time in cascades. The proposed approach possesses several unique characteristics compared to existing ones. Since its parameters are directly learned from cascade samples without requiring any additional information, it does not rely on any pre-existing diffusion structure. Because the solution to the diffusion equation can be expressed in a closed form in the projection space, the inference time for predicting the diffusion of a new piece of information is greatly reduced compared to discrete models. Experiments and comparisons with baselines and alternative models have been performed on both synthetic networks and real datasets. They show the effectiveness of the proposed method both in terms of prediction quality and of inference speed.