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Data Preparation

Our datasets are generated as the following procedures, following the practice of CIGA. The main difference in GALA is the generation of two-piece graphs, which is modified from SPMotif dataset generation.

Two-Piece Datasets

The global_b controls the spurious correlation strength while the label_noise controls the invariant correlation strength. The four datasets used in our paper are generated with

two-piece graph {0.8,0.6}:

  • global_b=0.6;
  • label_noise=0.2;

two-piece graph {0.8,0.7}:

  • global_b=0.7;
  • label_noise=0.2;

two-piece graph {0.8,0.9}:

  • global_b=0.90;
  • label_noise=0.2;

two-piece graph {0.7,0.9}:

  • global_b=0.9;
  • label_noise=0.3;

and then running the following command:

cd dataset_gen
python gen_basis.py

The generated data will be stored as in ./data/tSPMotif-{global_b} at the root directory of this repo. To use the dataset in main.py, specify the --dataset option and --bias option as mSPMotif and a corresponding bias, respectively. Feel free to play with other parameter settings!

DrugOOD Datasets

To obtain the DrugOOD datasets tested in our paper, i.e., drugood_lbap_core_ec50_assay, drugood_lbap_core_ec50_scaffold and drugood_lbap_core_ec50_size, we use the DrugOOD curation codes based on the commit eeb00b8da7646e1947ca7aec93041052a48bd45e and chembl_30 database. After curating the datasets, put the corresponding json files under ./data/DrugOOD, and specify the --dataset option as the corresponding dataset name to use, e.g., drugood_lbap_core_ec50_assay.

Similar operations can be performed to generate ki datasets.

CMNIST-sp

The CMNIST dataset is generated following the Invariant Risk Minimization and then converted into graphs using the SLIC superpixels algorithm. To generate the dataset, simply run the codes as the following:

cd dataset_gen
python prepare_mnist.py  --dataset 'cmnist'  -t 8 -s 'train'
python prepare_mnist.py  --dataset 'cmnist'  -t 8 -s 'test'

and the generated data will be put into ./data/CMNISTSP at the root directory of this repo. Note that two auxiliary datasets ./data/MNIST and ./data/ColoredMNIST will also be created as the base for the generation of ./data/CMNISTSP. To use the dataset, simply specify --dataset option as CMNIST.

Graph-SST2

Both of Graph-SST2 and Twitter are based on the datasets provided by DIG. To get the datasets, you may download via this link provided by DIG and the GNN explainability survey authors. Then unzip the data into ./data/Graph-SST2/raw. By specifying --dataset as the dataset name in main.py, the data loading process will add the degree biases automatically.

NCI1, NCI109, PROTEINS and DD

We use the datasets provided by size-invariant-GNNs authors, who already sampled the datasets with graph size distribution shifts injected. The datasets can be downloaed via this link. After downloading, simply unzip the datasets into ./data/TU. To use the datasets, simply specify --dataset as the dataset name in main.py.