Mixture of Mutually Exciting Processes for Viral Diffusion

Shuang-Hong Yang, Hongyuan Zha
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(2):1-9, 2013.

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 this Paper

BibTeX
@InProceedings{pmlr-v28-yang13a, title = {Mixture of Mutually Exciting Processes for Viral Diffusion}, author = {Yang, Shuang-Hong and Zha, Hongyuan}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {1--9}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/yang13a.pdf}, url = {https://proceedings.mlr.press/v28/yang13a.html}, 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.} }
Endnote
%0 Conference Paper %T Mixture of Mutually Exciting Processes for Viral Diffusion %A Shuang-Hong Yang %A Hongyuan Zha %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-yang13a %I PMLR %P 1--9 %U https://proceedings.mlr.press/v28/yang13a.html %V 28 %N 2 %X \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.
RIS
TY - CPAPER TI - Mixture of Mutually Exciting Processes for Viral Diffusion AU - Shuang-Hong Yang AU - Hongyuan Zha BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/05/13 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-yang13a PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 2 SP - 1 EP - 9 L1 - http://proceedings.mlr.press/v28/yang13a.pdf UR - https://proceedings.mlr.press/v28/yang13a.html AB - \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. ER -
APA
Yang, S. & Zha, H.. (2013). Mixture of Mutually Exciting Processes for Viral Diffusion. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(2):1-9 Available from https://proceedings.mlr.press/v28/yang13a.html.

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