Yang, S. ; Koeppl, H. (2018)
A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks.
AAAI 2018, Association for the Advancement of Artificial Intelligence. New Orleans (02.02.2018-07.02.2018)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We present a probabilistic model for learning from dynamic relational data, wherein the observed interactions among networked nodes are modeled via the Bernoulli Poisson link function, and the underlying network structure are characterized by nonnegative latent node-group memberships, which are assumed to be gamma distributed. The latent memberships evolve according to a Markov process. The optimal number of latent groups can be determined by data itself. The computational complexity of our method scales with the number of non-zero links, which makes it scalable to large sparse dynamic relational data. We present batch and online Gibbs sampling algorithms to perform model inference. Finally, we demonstrate the model’s performance on both synthetic and real-world datasets compared to state-of-the-art methods.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Yang, S. ; Koeppl, H. |
| Art des Eintrags: | Bibliographie |
| Titel: | A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks |
| Sprache: | Englisch |
| Publikationsjahr: | 2018 |
| Ort: | New Orleans |
| Veranstaltungstitel: | AAAI 2018, Association for the Advancement of Artificial Intelligence |
| Veranstaltungsort: | New Orleans |
| Veranstaltungsdatum: | 02.02.2018-07.02.2018 |
| Kurzbeschreibung (Abstract): | We present a probabilistic model for learning from dynamic relational data, wherein the observed interactions among networked nodes are modeled via the Bernoulli Poisson link function, and the underlying network structure are characterized by nonnegative latent node-group memberships, which are assumed to be gamma distributed. The latent memberships evolve according to a Markov process. The optimal number of latent groups can be determined by data itself. The computational complexity of our method scales with the number of non-zero links, which makes it scalable to large sparse dynamic relational data. We present batch and online Gibbs sampling algorithms to perform model inference. Finally, we demonstrate the model’s performance on both synthetic and real-world datasets compared to state-of-the-art methods. |
| Freie Schlagworte: | artificial intelligence, alogrithms, probabilistic model, network, computation, sparse dynamic, model interference |
| 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: | 15 Nov 2017 07:50 |
| Letzte Änderung: | 02 Jul 2024 09:16 |
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