Yang, S. ; Koeppl, H. (2018)
Dependent Relational Gamma Process Models for Longitudinal Networks.
Thirty-fifth International Conference on Machine Learning. Stockholm, Denmark (10.07.2018-15.07.2018)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
A probabilistic framework based on the covariate-dependent relational gamma process is developed to analyze relational data arising from longitudinal networks. The proposed framework characterizes networked nodes by nonnegative node-group memberships, which allow each node to belong to multiple latent groups simultaneously, and encodes edge probabilities between each pair of nodes using a Bernoulli Poisson link to the embedded latent space. Within the latent space, our framework models the birth and death dynamics of individual groups via a thinning function. Our framework also captures the evolution of individual node-group memberships over time using gamma Markov processes. Exploiting the recent advances in data augmentation and marginalization techniques, a simple and efficient Gibbs sampler is proposed for posterior computation. Experimental results on a simulation study and three real-world temporal network data sets demonstrate the model’s capability, competitive performance and scalability compared to state-of-the-art methods.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Yang, S. ; Koeppl, H. |
| Art des Eintrags: | Bibliographie |
| Titel: | Dependent Relational Gamma Process Models for Longitudinal Networks |
| Sprache: | Englisch |
| Publikationsjahr: | 15 Juli 2018 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Proceedings of Machine Learning Research |
| Buchtitel: | Proceedings of Machine Learning Research (PMLR) |
| Band einer Reihe: | 80 |
| Veranstaltungstitel: | Thirty-fifth International Conference on Machine Learning |
| Veranstaltungsort: | Stockholm, Denmark |
| Veranstaltungsdatum: | 10.07.2018-15.07.2018 |
| URL / URN: | https://icml.cc/Conferences/2018/Schedule?showEvent=1942 |
| Kurzbeschreibung (Abstract): | A probabilistic framework based on the covariate-dependent relational gamma process is developed to analyze relational data arising from longitudinal networks. The proposed framework characterizes networked nodes by nonnegative node-group memberships, which allow each node to belong to multiple latent groups simultaneously, and encodes edge probabilities between each pair of nodes using a Bernoulli Poisson link to the embedded latent space. Within the latent space, our framework models the birth and death dynamics of individual groups via a thinning function. Our framework also captures the evolution of individual node-group memberships over time using gamma Markov processes. Exploiting the recent advances in data augmentation and marginalization techniques, a simple and efficient Gibbs sampler is proposed for posterior computation. Experimental results on a simulation study and three real-world temporal network data sets demonstrate the model’s capability, competitive performance and scalability compared to state-of-the-art methods. |
| 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 Zentrale Einrichtungen Zentrale Einrichtungen > Centre for Cognitive Science (CCS) |
| Hinterlegungsdatum: | 01 Aug 2018 08:07 |
| Letzte Änderung: | 23 Sep 2021 14:30 |
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