Yang, S. ; Koeppl, H. (2020)
The Hawkes Edge Partition Model for Continuous-time Event-based
Temporal Networks.
36th Conference on Uncertainty in Artificial Intelligence (UAI). virtual Conference (03.08.2020-06.08.2020)
Konferenzveröffentlichung, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Yang, S. ; Koeppl, H. |
| Art des Eintrags: | Bibliographie |
| Titel: | The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks |
| Sprache: | Englisch |
| Publikationsjahr: | 3 August 2020 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI) |
| Veranstaltungstitel: | 36th Conference on Uncertainty in Artificial Intelligence (UAI) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 03.08.2020-06.08.2020 |
| URL / URN: | http://proceedings.mlr.press/v124/yang20a.html |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik |
| Hinterlegungsdatum: | 25 Mai 2020 10:13 |
| Letzte Änderung: | 03 Jul 2024 02:44 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks. (deposited 20 Jul 2022 13:41)
- The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks. (deposited 25 Mai 2020 10:13) [Gegenwärtig angezeigt]
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