GOODAT: Towards Test-time Graph Out-of-Distribution Detection

This is the source code of AAAI'24 paper "GOODAT: Towards Test-time Graph Out-of-Distribution Detection". This code builds on 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 put the code in ood/id folders into the root folder. For instance:

python AIDS.py

Statistic of Graph-level OOD Detection Benchmark

The statistic of each dataset pair in our benchmark is provided as follows. They are come from GOOD-D.

	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