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A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks

Yang, S. and Koeppl, H. (2018):
A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks.
In: AAAI 2018, Association for the Advancement of Artificial Intelligence, New Orleans, 2018, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2018
Creators: Yang, S. and Koeppl, H.
Title: A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks
Language: English
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.

Uncontrolled Keywords: artificial intelligence, alogrithms, probabilistic model, network, computation, sparse dynamic, model interference
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
18 Department of Electrical Engineering and Information Technology
Event Title: AAAI 2018, Association for the Advancement of Artificial Intelligence
Event Location: New Orleans
Event Dates: 2018
Date Deposited: 15 Nov 2017 07:50
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