TU Darmstadt / ULB / TUbiblio

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

Yang, S. and Koeppl, H. (2020):
The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks.
pp. 460-469, 36th Conference on Uncertainty in Artificial Intelligence (UAI), virtual Conference, August 03.-06., 2020, ISSN 2640-3498,
[Conference or Workshop Item]

Abstract

We propose a novel probabilistic framework to model continuously generated interaction events data. Our goal is to infer the 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 P´olya-Gamma data augmentation strategy. Experimental results on real-world datasets demonstrate that our model not only achieves competitive performance compared with state-of-theart methods, but also discovers interpretable latent structure behind the observed temporal interactions.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Yang, S. and Koeppl, H.
Title: The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks
Language: English
Abstract:

We propose a novel probabilistic framework to model continuously generated interaction events data. Our goal is to infer the 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 P´olya-Gamma data augmentation strategy. Experimental results on real-world datasets demonstrate that our model not only achieves competitive performance compared with state-of-theart methods, but also discovers interpretable latent structure behind the observed temporal interactions.

Journal or Publication Title: Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI)
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
Event Title: 36th Conference on Uncertainty in Artificial Intelligence (UAI)
Event Location: virtual Conference
Event Dates: August 03.-06., 2020
Date Deposited: 25 May 2020 10:13
Official URL: http://proceedings.mlr.press/v124/yang20a.html
Corresponding Links:
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details