Mixture of Mutually Exciting Processes for Viral Diffusion

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Mixture of Mutually Exciting Processes for Viral Diffusion

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by Shuang-hong Yang , Hongyuan Zha

Citations:

12 - 3 self

BibTeX

@MISC{Yang_mixtureof,
    author = {Shuang-hong Yang and Hongyuan Zha},
    title = {Mixture of Mutually Exciting Processes for Viral Diffusion},
    year = {}
}

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Abstract

Diffusion network inference and 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. 1.

Keyphrases

viral diffusion mutually exciting process meme tracking mean-field variational inference probabilistic model viral text social network key challenge exciting point process diffusion network inference diffusion bandwidth fast learning algorithm

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