Differential equation and probability inspired graph neural networks for latent variable learning

This repository consists some models with respect to Differential equation and probability inspired graph neural networks for latent variable learning.

Contents

• gcn-lp-filter consists some models for node classification.
• graph-classifier-dgl consists some models for graph pooling and classification, using some examples of DGL.
• graph-classifier-vi consists some models combining variational inference models (e.g. VGAE, Planar flow, Normalizing flow, Inverse autoregressive flow, etc.) to graph neural networks for graph pooling and classification, based on Graph U-Nets .

Citation

@article{shi2022latentgnn,
  title={Differential equation and probability inspired graph neural networks for latent variable learning},
  author={Zhuangwei Shi},
  journal={arXiv preprint arXiv:2202.13800},
  year={2022},
}