Yang, S. and Koeppl, H. (2018):
Dependent Relational Gamma Process Models for Longitudinal Networks.
80, In: Proceedings of Machine Learning Research (PMLR), pp. 5547-5556,
Thirty-fifth International Conference on Machine Learning, Stockholm, Denmark, July 10-15, 2018, [Conference or Workshop Item]
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.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2018 |
| Creators: | Yang, S. and Koeppl, H. |
| Title: | Dependent Relational Gamma Process Models for Longitudinal Networks |
| Language: | English |
| 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. |
| Journal or Publication Title: | Proceedings of Machine Learning Research |
| Title of Book: | Proceedings of Machine Learning Research (PMLR) |
| Volume: | 80 |
| Divisions: | 18 Department of Electrical Engineering and Information Technology 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 Zentrale Einrichtungen Zentrale Einrichtungen > Centre for Cognitive Science (CCS) |
| Event Title: | Thirty-fifth International Conference on Machine Learning |
| Event Location: | Stockholm, Denmark |
| Event Dates: | July 10-15, 2018 |
| Date Deposited: | 01 Aug 2018 08:07 |
| Official URL: | https://icml.cc/Conferences/2018/Schedule?showEvent=1942 |
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