• Corpus ID: 221397582

Stochastic Graph Recurrent Neural Network

@article{Yan2020StochasticGR,
  title={Stochastic Graph Recurrent Neural Network},
  author={Tijin Yan and Hongwei Zhang and Zirui Li and Yuanqing Xia},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.00538}
}
Representation learning over graph structure data has been widely studied due to its wide application prospects. However, previous methods mainly focus on static graphs while many real-world graphs evolve over time. Modeling such evolution is important for predicting properties of unseen networks. To resolve this challenge, we propose SGRNN, a novel neural architecture that applies stochastic latent variables to simultaneously capture the evolution in node attributes and topology. Specifically… 

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