Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling

• URL: http://arxiv.org/abs/2002.10235v2
• Date: Wed, 29 Apr 2020 10:54:50 GMT
• Title: Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling
• Abstract summary: Dirichlet Belief Network(DirBN) has been recently proposed as a promising approach in learning interpretable deep latent representations for objects. We propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data.
• Score: 29.138765941440145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. 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.

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