Skip to content

Latest commit

 

History

History
75 lines (56 loc) · 3.11 KB

README.md

File metadata and controls

75 lines (56 loc) · 3.11 KB
Raw

A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"

  • This is a Python3 implementation of generalised message-passing framework (GMP) and GraphSNN neural model for node classification and graph classification.

Prerequisites

  • torch (>= 1.7.1)
  • keras (>= 2.2.2)
  • tensorflow (>= 1.9.0)
  • torch_geometric (>= 2.0.1)
  • ogb (>= 1.3.1)
  • networkx (>= 2.2)
  • numpy (>=1.19.0)
  • scipy (>=1.2.1)
  • cython (>=0.27.3)

Evaluation on node classification

* Files description
	* graphsn_standard_splits_node_classification.ipynb - Node classification with standard splits
	* graphsn_random_splits_node_classification.ipynb - Node classification with random splits
	* utils.py - Data preprocessing and loading the data
	* models.py - N-layer GNN model with GraphSNN_M for node classification
	* layers.py - GraphSNN_M layer

* For node classification tasks, we use four citation network datasets:
	[1] Cora, Citeseer and Pubmed for semi-supervised document classification
	[2] NELL for semi-supervised entity classification

* The experimental results show that our method consistently outperforms all state-of-the-art methods on all benchmark datasets.
* We consider the four popular message-passing GNNs: GCN, GAT, GIN, and GraphSAGE.

Evaluation on large graph classification (OGB graph dataset)

* Files description
	* ogbg_mol.ipynb - GraphSNN evaluation on OGB graph dataset
	* conv.py - GraphSNN convolution along the graph structure
	* gnn.py - GraphSNN pooling function to generate whole-graph embeddings
    
* For large graph classification tasks, we use five large graph datasets from Open Graph Benchmark (OGB), 
including four molecular graph datasets (ogbg-molhiv, ogbg-moltox21, ogbg-moltoxcast and ogb-molpcba) and one protein-protein association network (ogbg-ppa). 
We also consider a variant, denoted as GraphSNN+VN, which performs the message passing over augmented graphs with virtual nodes in GraphSNN.

Evaluation on small graph classification

* Files description
	* graphsn_graph_classification.ipynb - GraphSNN cross validation
	* graph_data.py - Data preprocessing and loading the data
	* data_reader.py - Read the txt files containing all data of the dataset
	* models.py - GNN model with multiple GraphSNN layers for constructing the readout function
	* layers.py - GraphSNN layer
    
* We evaluate our model on standard stratified splits and random splits. We use eight benchmark datasets grouped in two categories.
	(1) 6 from bioinformatics datasets - MUTAG, PTC-MR, COX2, BZR, PROTEINS, and D&D
	(2) 2 from social network datasets - IMDB-B and RDT-M5K

Citation

Please cite our paper if you use this code in your research work.

@inproceedings{wijesinghe2021iclr,
  title={A New Perspective on" How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"},
  author={Wijesinghe, Asiri and Wang, Qing},
  booktitle={International Conference on Learning Representations},
  year={2022}
}

License

MIT License

Contact for GraphSNN issues

Please contact me: asiri.wijesinghe@anu.edu.au if you have any questions or submit a Github issue if you find any bugs.