COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Evolution
Abstract
Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly intertwined stochastic processes, information diffusion and network evolution, have been predominantly studied separately, ignoring their co-evolutionary dynamics.
Cite
Text
Farajtabar et al. "COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Evolution." Journal of Machine Learning Research, 2017.Markdown
[Farajtabar et al. "COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Evolution." Journal of Machine Learning Research, 2017.](https://mlanthology.org/jmlr/2017/farajtabar2017jmlr-coevolve/)BibTeX
@article{farajtabar2017jmlr-coevolve,
title = {{COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Evolution}},
author = {Farajtabar, Mehrdad and Wang, Yichen and Gomez-Rodriguez, Manuel and Li, Shuang and Zha, Hongyuan and Song, Le},
journal = {Journal of Machine Learning Research},
year = {2017},
pages = {1-49},
volume = {18},
url = {https://mlanthology.org/jmlr/2017/farajtabar2017jmlr-coevolve/}
}