Learning Triggering Kernels for Multi-Dimensional Hawkes Processes
Abstract
How does the activity of one person affect that of another person? Does the strength of influence remain periodic or decay exponentially over time? In this paper, we study these critical questions in social network analysis quantitatively under the framework of multi-dimensional Hawkes processes. In particular, we focus on the nonparametric learning of the triggering kernels, and propose an algorithm \sf MMEL that combines the idea of decoupling the parameters through constructing a tight upper-bound of the objective function and application of Euler-Lagrange equations for optimization in infinite dimensional functional space. We show that the proposed method performs significantly better than alternatives in experiments on both synthetic and real world datasets.
Cite
Text
Zhou et al. "Learning Triggering Kernels for Multi-Dimensional Hawkes Processes." International Conference on Machine Learning, 2013.Markdown
[Zhou et al. "Learning Triggering Kernels for Multi-Dimensional Hawkes Processes." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/zhou2013icml-learning/)BibTeX
@inproceedings{zhou2013icml-learning,
title = {{Learning Triggering Kernels for Multi-Dimensional Hawkes Processes}},
author = {Zhou, Ke and Zha, Hongyuan and Song, Le},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {1301-1309},
volume = {28},
url = {https://mlanthology.org/icml/2013/zhou2013icml-learning/}
}