Mixture of Mutually Exciting Processes for Viral Diffusion
Abstract
\emphDiffusion network inference and \emphmeme 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.
Cite
Text
Yang and Zha. "Mixture of Mutually Exciting Processes for Viral Diffusion." International Conference on Machine Learning, 2013.Markdown
[Yang and Zha. "Mixture of Mutually Exciting Processes for Viral Diffusion." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/yang2013icml-mixture/)BibTeX
@inproceedings{yang2013icml-mixture,
title = {{Mixture of Mutually Exciting Processes for Viral Diffusion}},
author = {Yang, Shuang-Hong and Zha, Hongyuan},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {1-9},
volume = {28},
url = {https://mlanthology.org/icml/2013/yang2013icml-mixture/}
}