The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks

Sikun Yang, Heinz Koeppl
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:460-469, 2020.

Abstract

We propose a novel probabilistic framework to model continuously generated interaction events data. Our goal is to infer the \emph{implicit} community structure underlying the temporal interactions among entities, and also to exploit how the latent structure influence their interaction dynamics. To this end, we model the reciprocating interactions between individuals using mutually-exciting Hawkes processes. The base rate of the Hawkes process for each pair of individuals is built upon the latent representations inferred using the hierarchical gamma process edge partition model (HGaP-EPM). In particular, our model allows the interaction dynamics between each pair of individuals to be modulated by their respective affiliated communities.Moreover, our model can flexibly incorporate the auxiliary individuals’ attributes, or covariates associated with interaction events. Efficient Gibbs sampling and Expectation-Maximization algorithms are developed to perform inference via Pólya-Gamma data augmentation strategy. Experimental results on real-world datasets demonstrate that our model not only achieves competitive performance compared with state-of-the-art methods, but also discovers interpretable latent structure behind the observed temporal interactions.

Cite this Paper


BibTeX
@InProceedings{pmlr-v124-yang20a, title = {The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks}, author = {Yang, Sikun and Koeppl, Heinz}, booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)}, pages = {460--469}, year = {2020}, editor = {Jonas Peters and David Sontag}, volume = {124}, series = {Proceedings of Machine Learning Research}, month = {03--06 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v124/yang20a/yang20a.pdf}, url = { http://proceedings.mlr.press/v124/yang20a.html }, abstract = {We propose a novel probabilistic framework to model continuously generated interaction events data. Our goal is to infer the \emph{implicit} community structure underlying the temporal interactions among entities, and also to exploit how the latent structure influence their interaction dynamics. To this end, we model the reciprocating interactions between individuals using mutually-exciting Hawkes processes. The base rate of the Hawkes process for each pair of individuals is built upon the latent representations inferred using the hierarchical gamma process edge partition model (HGaP-EPM). In particular, our model allows the interaction dynamics between each pair of individuals to be modulated by their respective affiliated communities.Moreover, our model can flexibly incorporate the auxiliary individuals’ attributes, or covariates associated with interaction events. Efficient Gibbs sampling and Expectation-Maximization algorithms are developed to perform inference via Pólya-Gamma data augmentation strategy. Experimental results on real-world datasets demonstrate that our model not only achieves competitive performance compared with state-of-the-art methods, but also discovers interpretable latent structure behind the observed temporal interactions.} }
Endnote
%0 Conference Paper %T The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks %A Sikun Yang %A Heinz Koeppl %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-yang20a %I PMLR %P 460--469 %U http://proceedings.mlr.press/v124/yang20a.html %V 124 %X We propose a novel probabilistic framework to model continuously generated interaction events data. Our goal is to infer the \emph{implicit} community structure underlying the temporal interactions among entities, and also to exploit how the latent structure influence their interaction dynamics. To this end, we model the reciprocating interactions between individuals using mutually-exciting Hawkes processes. The base rate of the Hawkes process for each pair of individuals is built upon the latent representations inferred using the hierarchical gamma process edge partition model (HGaP-EPM). In particular, our model allows the interaction dynamics between each pair of individuals to be modulated by their respective affiliated communities.Moreover, our model can flexibly incorporate the auxiliary individuals’ attributes, or covariates associated with interaction events. Efficient Gibbs sampling and Expectation-Maximization algorithms are developed to perform inference via Pólya-Gamma data augmentation strategy. Experimental results on real-world datasets demonstrate that our model not only achieves competitive performance compared with state-of-the-art methods, but also discovers interpretable latent structure behind the observed temporal interactions.
APA
Yang, S. & Koeppl, H.. (2020). The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in Proceedings of Machine Learning Research 124:460-469 Available from http://proceedings.mlr.press/v124/yang20a.html .

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