Yang, Sikun ; Koeppl, Heinz (2022)
The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks.
Conference on Uncertainty in Artificial Intelligence (UAI). Online (03.08.2020-06.08.2020)
doi: 10.26083/tuprints-00021515
Konferenzveröffentlichung, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Yang, Sikun ; Koeppl, Heinz |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2020 |
| Verlag: | PMLR |
| Buchtitel: | Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) |
| Reihe: | Proceedings of Machine Learning Research |
| Band einer Reihe: | 124 |
| Veranstaltungstitel: | Conference on Uncertainty in Artificial Intelligence (UAI) |
| Veranstaltungsort: | Online |
| Veranstaltungsdatum: | 03.08.2020-06.08.2020 |
| DOI: | 10.26083/tuprints-00021515 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/21515 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (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. |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-215150 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| 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 18 Fachbereich Elektrotechnik und Informationstechnik > Self-Organizing Systems Lab |
| Hinterlegungsdatum: | 20 Jul 2022 13:41 |
| Letzte Änderung: | 21 Jul 2022 13:30 |
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| Suche nach Titel in: | TUfind oder in Google |
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- The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks. (deposited 20 Jul 2022 13:41) [Gegenwärtig angezeigt]
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