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Dependent Relational Gamma Process Models for Longitudinal Networks

Yang, S. ; Koeppl, H. (2018)
Dependent Relational Gamma Process Models for Longitudinal Networks.
Thirty-fifth International Conference on Machine Learning. Stockholm, Denmark (July 10-15, 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: July 10-15, 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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