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Scalable Deep Generative Relational Model with High-Order Node Dependence
Xuhui Fan · Bin Li · Caoyuan Li · Scott SIsson · Ling Chen

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #149

In this work, we propose a probabilistic framework for relational data modelling and latent structure exploring. Given the possible feature information for the nodes in a network, our model builds up a deep architecture that can approximate to the possible nonlinear mappings between the nodes' feature information and latent representations. For each node, we incorporate all its neighborhoods' high-order structure information to generate latent representation, such that these latent representations are ``smooth'' in terms of the network. Since the latent representations are generated from Dirichlet distributions, we further develop a data augmentation trick to enable efficient Gibbs sampling for Ber-Poisson likelihood with Dirichlet random variables. Our model can be ready to apply to large sparse network as its computations cost scales to the number of positive links in the networks. The superior performance of our model is demonstrated through improved link prediction performance on a range of real-world datasets.

Author Information

Xuhui Fan (University of New South Wales)
Bin Li (Fudan University)
Caoyuan Li (UTS)
Scott SIsson (University of New South Wales, Sydney)
Ling Chen (" University of Technology, Sydney, Australia")

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