GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection

This is the source code of WSDM'23 paper "GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection".

Requirements

This code requires the following:

• Python==3.9
• Pytorch==1.11.0
• Pytorch Geometric==2.0.4
• Numpy==1.21.2
• Scikit-learn==1.0.2
• OGB==1.3.3
• NetworkX==2.7.1
• FAISS-GPU==1.7.2

Usage

Just run the script corresponding to the experiment and dataset you want. For instance:

• Run out-of-distribution detection on BZR (ID) and COX2 (OOD) datasets:

bash script/oodd_BZR+COX2.sh

• Run anomaly detection on PROTEINS_full datasets:

bash script/ad_PROTEINS_full.sh

Statistic of Graph-level OOD Detection Benchmark

The statistic of each dataset pair in our benchmark is provided as follows.

	ID dataset				OOD dataset
No.	Name	# Graph (Train/Test)	# Node (avg.)	# Edge (avg.)	Name	# Graph (Test)	# Node (avg.)	# Edge (avg.)
1	BZR	364/41	35.8	38.4	COX2	41	41.2	43.5
2	PTC-MR	309/35	14.3	14.7	MUTAG	35	17.9	19.8
3	AIDS	1,800/200	15.7	16.2	DHFR	200	42.4	44.5
4	ENZYMES	540/60	32.6	62.1	PROTEIN	60	39.1	72.8
5	IMDB-B	1,350/150	19.8	96.5	IMDB-M	150	13.0	65.9
6	Tox21	7,047/784	18.6	19.3	SIDER	784	33.6	35.4
7	FreeSolv	577/65	8.7	8.4	ToxCast	65	18.8	19.3
8	BBBP	1,835/204	24.1	26.0	BACE	204	34.1	36.9
9	ClinTox	1,329/148	26.2	27.9	LIPO	148	27.0	29.5
10	Esol	1,015/113	13.3	13.7	MUV	113	24.2	26.3

Statistic of Graph-level Anomaly Detection Datasets

The statistic of each dataset in the anomaly detection experiments is provided as follows.

Dataset	# Graph (Train/Test)	# Node (avg.)	# Edge (avg.)
PROTEINS-full	360/223	39.1	72.8
ENZYMES	400/120	32.6	62.1
AIDS	1280/400	15.7	16.2
DHFR	368/152	42.4	44.5
BZR	69/81	35.8	38.4
COX2	81/94	41.2	43.5
DD	390/236	284.3	715.7
NCI1	1646/822	29.8	32.3
IMDB-B	400/200	19.8	96.5
REDDIT-B	800/400	429.6	497.8
COLLAB	1920/1000	74.5	2457.8
HSE	423/267	16.9	17.2
MMP	6170/238	17.6	18.0
p53	8088/269	17.9	18.3
PPAR-gamma	219/267	17.4	17.7

Implementation Details

Hyper-parameters

For the sake of efficiency, we set the structural encoding dimensions $d_s^{(rw)}$ and $d_s^{(dg)}$ to $16$. The encoders are 5-layer GINs with $16$ hidden dimensions. The number of dimensions of projected embeddings is the same as which of node embeddings. The batch size is selected from $16$ to $128$ according to the graph size of datasets. The number of clusters $K$ and self-adaptiveness parameter $\alpha$ are selected through grid search, with the scopes of ${2, 3, 5, 10, 15, 20, 30}$ and ${0, 0.2, 0.4, 0.6, 0.8, 1.0}$, respectively. The model is trained by the Adam optimizer with a learning rate of $0.0001$ until converging.

Computing Infrastructures

We conduct the experiments on a Linux server with an Intel Xeon Gold 6226R CPU and two Tesla V100S GPUs. We implement our method with PyTorch 1.11.0 and Pytorch Geometric 2.0.4.

Cite

If you compare with, build on, or use aspects of this work, please cite the following:

@inproceedings{liu2023goodd,
  title={GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection},
  author={Liu, Yixin and Ding, Kaize and Liu, Huan and Pan, Shirui},
  booktitle={Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining},
  year={2023}
}