- Title
- Recurrent Dirichlet Belief Networks for interpretable dynamic relational data modelling
- Creator
- Li, Yaqiong; Fan, Xuhui; Chen, Ling; Li, Bin; Yu, Zheng; Sisson, Scott A.
- Relation
- Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20). Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) (Yokohama, Japan January 2021) p. 2470-2476
- Relation
- ARC.DP180100966 http://purl.org/au-research/grants/arc/DP180100966
- Publisher Link
- http://dx.doi.org/10.24963/ijcai.2020/342
- Publisher
- International Joint Conferences on Artificial Intelligence
- Resource Type
- conference paper
- Date
- 2020
- Description
- The Dirichlet Belief Network (DirBN) has been recently proposed as a promising approach in learning interpretable deep latent representations for objects. In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework - the Recurrent Dirichlet Belief Network (Recurrent-DBN) - to study interpretable hidden structures from dynamic relational data. The proposed Recurrent-DBN has the following merits: (1) it infers interpretable and organised hierarchical latent structures for objects within and across time steps; (2) it enables recurrent long-term temporal dependence modelling, which outperforms the one-order Markov descriptions in most of the dynamic probabilistic frameworks; (3) the computational cost scales to the number of positive links only. In addition, we develop a new inference strategy, which first upward- and-backward propagates latent counts and then downward-and-forward samples variables, to enable efficient Gibbs sampling for the Recurrent-DBN. We apply the Recurrent-DBN to dynamic relational data problems. The extensive experiment results on real-world data validate the advantages of the Recurrent-DBN over the state-of-the-art models in interpretable latent structure discovery and improved link prediction performance.
- Subject
- machine learning; bayesian networks; data modelling; dynamic data
- Identifier
- http://hdl.handle.net/1959.13/1441785
- Identifier
- uon:41530
- Identifier
- ISBN:9780999241165
- Language
- eng
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