Search Machine Learning Repository: Mixture of Mutually Exciting Processes for Viral Diffusion
Authors: Shuang-hong Yang and Hongyuan Zha
Conference: Proceedings of the 30th International Conference on Machine Learning (ICML-13)
Year: 2013
Pages: 1-9
Abstract: \emph{Diffusion network inference} and \emph{meme tracking} have been two key challenges in viral diffusion. This paper shows that these two tasks can be addressed simultaneously with a probabilistic model involving a mixture of mutually exciting point processes. A fast learning algorithms is developed based on mean-field variational inference with budgeted diffusion bandwidth. The model is demonstrated with applications to the diffusion of viral texts in (1) online social networks (e.g., Twitter) and (2) the blogosphere on the Web.
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