GraphSAINT

This DGL example implements the paper: GraphSAINT: Graph Sampling Based Inductive Learning Method.

Paper link: https://arxiv.org/abs/1907.04931

Author's code: https://github.com/GraphSAINT/GraphSAINT

Contributor: Liu Tang (@lt610)

Dependencies

• Python 3.7.0
• PyTorch 1.6.0
• NumPy 1.19.2
• Scikit-learn 0.23.2
• DGL 0.5.3

Dataset

All datasets used are provided by Author's code. They are available in Google Drive (alternatively, Baidu Wangpan (code: f1ao)). Once you download the datasets, you need to rename graphsaintdata to data. Dataset summary("m" stands for multi-label classification, and "s" for single-label.):

Dataset	Nodes	Edges	Degree	Feature	Classes	Train/Val/Test
PPI	14,755	225,270	15	50	121(m)	0.66/0.12/0.22
Flickr	89,250	899,756	10	500	7(s)	0.50/0.25/0.25
Reddit	232,965	11,606,919	50	602	41(s)	0.66/0.10/0.24
Yelp	716,847	6,877,410	10	300	100(m)	0.75/0.10/0.15
Amazon	1,598,960	132,169,734	83	200	107(m)	0.85/0.05/0.10

Minibatch training

Run with following:

python train_sampling.py --gpu 0 --dataset ppi --sampler node --node-budget 6000 --num-repeat 50 --n-epochs 1000 --n-hidden 512 --arch 1-0-1-0
python train_sampling.py --gpu 0 --dataset ppi --sampler edge --edge-budget 4000 --num-repeat 50 --n-epochs 1000 --n-hidden 512 --arch 1-0-1-0 --dropout 0.1
python train_sampling.py --gpu 0 --dataset ppi --sampler rw --num-roots 3000 --length 2 --num-repeat 50 --n-epochs 1000 --n-hidden 512 --arch 1-0-1-0 --dropout 0.1
python train_sampling.py --gpu 0 --dataset flickr --sampler node --node-budget 8000 --num-repeat 25 --n-epochs 30 --n-hidden 256 --arch 1-1-0 --dropout 0.2
python train_sampling.py --gpu 0 --dataset flickr --sampler edge --edge-budget 6000 --num-repeat 25 --n-epochs 15 --n-hidden 256 --arch 1-1-0 --dropout 0.2
python train_sampling.py --gpu 0 --dataset flickr --sampler rw --num-roots 6000 --length 2 --num-repeat 25 --n-epochs 15 --n-hidden 256 --arch 1-1-0 --dropout 0.2
python train_sampling.py --gpu 0 --dataset reddit --sampler node --node-budget 8000 --num-repeat 50 --n-epochs 40 --n-hidden 128 --arch 1-0-1-0 --dropout 0.1
python train_sampling.py --gpu 0 --dataset reddit --sampler edge --edge-budget 6000 --num-repeat 50 --n-epochs 40 --n-hidden 128 --arch 1-0-1-0 --dropout 0.1
python train_sampling.py --gpu 0 --dataset reddit --sampler rw --num-roots 2000 --length 4 --num-repeat 50 --n-epochs 30 --n-hidden 128 --arch 1-0-1-0 --dropout 0.1
python train_sampling.py --gpu 0 --dataset yelp --sampler node --node-budget 5000 --num-repeat 50 --n-epochs 50 --n-hidden 512 --arch 1-1-0 --dropout 0.1
python train_sampling.py --gpu 0 --dataset yelp --sampler edge --edge-budget 2500 --num-repeat 50 --n-epochs 100 --n-hidden 512 --arch 1-1-0 --dropout 0.1
python train_sampling.py --gpu 0 --dataset yelp --sampler rw --num-roots 1250 --length 2 --num-repeat 50 --n-epochs 75 --n-hidden 512 --arch 1-1-0 --dropout 0.1
python train_sampling.py --gpu 0 --dataset amazon --sampler node --node-budget 4500 --num-repeat 50 --n-epochs 30 --n-hidden 512 --arch 1-1-0 --dropout 0.1
python train_sampling.py --gpu 0 --dataset amazon --sampler edge --edge-budget 2000 --num-repeat 50 --n-epochs 30 --n-hidden 512 --arch 1-1-0 --dropout 0.1
python train_sampling.py --gpu 0 --dataset amazon --sampler rw --num-roots 1500 --length 2 --num-repeat 50 --n-epochs 30 --n-hidden 512 --arch 1-1-0 --dropout 0.1

Comparison

• Paper: results from the paper
• Running: results from experiments with the authors' code
• DGL: results from experiments with the DGL example

F1-micro

Random node sampler

Method	PPI	Flickr	Reddit	Yelp	Amazon
Paper	0.960±0.001	0.507±0.001	0.962±0.001	0.641±0.000	0.782±0.004
Running	0.9628	0.5077	0.9622	0.6393	0.7695
DGL	0.9618	0.4828	0.9621	0.6360	0.7748

Random edge sampler

Method	PPI	Flickr	Reddit	Yelp	Amazon
Paper	0.981±0.007	0.510±0.002	0.966±0.001	0.653±0.003	0.807±0.001
Running	0.9810	0.5066	0.9656	0.6531	0.8071
DGL	0.9818	0.5054	0.9653	0.6517	exceed

Random walk sampler

Method	PPI	Flickr	Reddit	Yelp	Amazon
Paper	0.981±0.004	0.511±0.001	0.966±0.001	0.653±0.003	0.815±0.001
Running	0.9812	0.5104	0.9648	0.6527	0.8131
DGL	0.9818	0.5018	0.9649	0.6516	0.8150

Sampling time

Random node sampler

Method	PPI	Flickr	Reddit	Yelp	Amazon
Sampling(Running)	0.77	0.65	7.46	26.29	571.42
Sampling(DGL)	0.24	0.57	5.06	30.04	163.75
Normalization(Running)	0.69	2.84	11.54	32.72	407.20
Normalization(DGL)	1.04	0.41	21.05	68.63	2006.94

Random edge sampler

Method	PPI	Flickr	Reddit	Yelp	Amazon
Sampling(Running)	0.72	0.56	4.46	12.38	101.76
Sampling(DGL)	0.50	0.72	53.88	254.63	exceed
Normalization(Running)	0.68	2.62	9.42	26.64	62.59
Normalization(DGL)	0.61	0.38	14.69	23.63	exceed

Random walk sampler

Method	PPI	Flickr	Reddit	Yelp	Amazon
Sampling(Running)	0.83	1.22	6.69	18.84	209.83
Sampling(DGL)	0.28	0.63	4.02	22.01	55.09
Normalization(Running)	0.87	2.60	10.28	24.41	145.85
Normalization(DGL)	0.70	0.42	18.34	32.16	683.96