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

Yang, Sikun ; Koeppl, Heinz (2022):
The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks. (Publisher's Version)
In: Proceedings of Machine Learning Research, 124, In: Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 460-469,
Darmstadt, PMLR, Conference on Uncertainty in Artificial Intelligence (UAI), Online, 03.-06.08.2020, DOI: 10.26083/tuprints-00021515,
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Item Type: Conference or Workshop Item
Origin: Secondary publication service
Status: Publisher's Version
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 \emphimplicit 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.

Book Title: Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)
Series: Proceedings of Machine Learning Research
Series Volume: 124
Place of Publication: Darmstadt
Publisher: PMLR
Classification DDC: 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Self-Organizing Systems Lab
Event Title: Conference on Uncertainty in Artificial Intelligence (UAI)
Event Location: Online
Event Dates: 03.-06.08.2020
Date Deposited: 20 Jul 2022 13:41
Last Modified: 20 Jul 2022 13:41
DOI: 10.26083/tuprints-00021515
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-215150
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21515
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