The official code of the paper A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests.
- 02/13: initial commit.
- 02/15: add training logs on ZINC-subset and ZINC-full datasets.
- 03/21: add training logs on coutning substructure dataset and ogbg-molhiv dataset.
- 05/02: relase the full code.
- 06/19: update README.
Required packages:
- torch 1.9.0
- torch-cluster 1.5.9
- torch-scatter 2.0.8
- torch-sparse 0.6.11
- torch-spline-conv 1.2.1
- torch-geometric 1.7.2
See config files in the config folder.
Each log file in the logs folder contains the training result of a model configuration. In each file, the i-th row contains metrics at the i-th training epoch, typically, the learning rate, training loss, validation MAE, and test MAE.
Each file is named by the corresponding dataset (ZINC-subset or ZINC-full), model string, batch size, distance encoding hyper-parameter, and random seed, with the form logs/ZINC/<subset>.<model>.<batch size>.<max distance>.<seed>.txt. We also rerun the baseline SUN architecture on the ZINC-full dataset. The logs are saved in logs/ZINC/<policy>.<model>.<seed>.txt.
Each file is named by the model string, task name, network hidden size, batch size, learning rate, epochs, distance encoding hyper-parameter, and random seed, with the form logs/substructure/<model>-GraphCount-<task>-<hidden size>-<batch size>-<learning rate>-<epochs>-<max distance>-<seed>.txt. We also rerun the baseline SUN architecture for counting 5 cycle and 6-cycle. The logs are saved in logs/substructure/<policy>-<hop k>-<task>.csv.
All log files are in the logs/ogbg-molhiv folder.
Our code is motivated by the code of SUN.
@inproceedings{zhang2023complete,
author = {Zhang, Bohang and Feng, Guhao and Du, Yiheng and He, Di and Wang, Liwei},
title = {A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests},
booktitle = {International Conference on Machine Learning},
year = {2023},
}