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