SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 9: Statistics of datasets for pre-training
Dataset	Academia	DBLP(SNALP)	DBLP(NetRep)	IMDB	Facebook	LiveJournal
|V|	137,969	317,080	540,486	896,305	3,097,165	4,843,953
|E|	739,984	2,099,732	30,491,158	7,564,894	47,334,788	85,691,368
Table 10: Test accuracy (%) performance of GCC on graph classification benchmarks.
Pre-training Strategy	Tuning Strategy	IMDB-B	IMDB-M	Avg.
GCC (E2E)	FT	72.60 ±4.72	49.07 ±3.59	60.83
GPF	73.40 ±3.80	49.17 ±3.12	61.28
GCC (MoCo)	FT	71.70 ±4.98	48.07 ±2.91	59.88
GPF	72.50 ±3.20	49.33 ±3.93	60.91
B.8 Hyper-parameter settings
This section presents the hyper-parameters used during the adaptation stage of pre-trained GNN models on downstream tasks for our proposed graph prompt tuning. Table 11 summarizes the hyper-parameter settings. You can also visit our code repository to obtain the specific commands for reproducing the experimental results.
Table 11: The hyper-parameter settings.
Dataset	Pre-training Strategy	Prompt Dimension	Learning Rate	Weight Decay	Batch Size	Training Epoch
Biology	Infomax	300	0.001	0	32	50
EdgePred	300	0.001	0	32	50
Masking	300	0.001	0	32	50
ContextPred	300	0.001	0	32	50
GCL	300	0.0001	0	32	50
Chemistry	Infomax	300	0.001	0	32	100
EdgePred	300	0.001	0	32	100
Masking	300	0.001	0	32	100
ContextPred	300	0.001	0	32	100
GCL	300	0.001	0	32	100
IMDB-B	GCC	64	0.005	Linear	128	100
IMDB-M	GCC	64	0.005	Linear	128	100