/gnn_hierarchical_pooling

Graph Neural Network with Hierarchical Pooling for PyTorch: "Hierarchical Graph Representation Learning with Differentiable Pooling".

Primary LanguagePython

Hierarchical Graph Representation Learning

Requirements

  • Python==3.6.x
  • PyTorch==1.1.0
  • NumPy>=1.16.3
  • matplotlib>=3.0.3
  • networkx==2.4
  • tensorboardX==1.8

DataSet & Task

ENZYMES dataset is used, which includes 600 molecule structures. The edge feature denotes whether there is a connection between two molecules and the node feature denotes what kind of element for a node.

  • Classify the type of enzyme
  • 600 samples (training: 540, testing: 60)
  • 6 types; 100 samples per type
  • the number of nodes per graph: 2 ~ 125 (median value ~ 30)
  • dimension of node features: 3

Model Structure

Usage

python train.py --hparam_path=./config/hparams_testdb.yml # or other config files you defined

Results

Reported Results

Replication

Best val result: 0.6133 @ epoch 765

Reference

[1] Ying, Zhitao, et al. "Hierarchical graph representation learning with differentiable pooling." Advances in Neural Information Processing Systems. 2018.

[2] Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.