GraphSAINT-NRW, ERW

• Juyeong Shin, Young-Koo Lee

• KSC 2022 Paper

• Paper link: https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11224420

• Modified from DGL GraphSAINT example

  • Paper link: https://arxiv.org/abs/1907.04931
  • Author's code: https://github.com/GraphSAINT/GraphSAINT
  • DGL example code: https://github.com/dmlc/dgl/tree/master/examples

Dependencies

• Python 3.10.12
• PyTorch 2.0.1
• NumPy 1.25.0
• Scikit-learn 1.2.2
• DGL 1.1.1

Dataset

All datasets used are provided by Author's code. They are available in Google Drive (alternatively, Baidu Wangpan (code: f1ao)).

Config

• The config file is config.py, which contains best configs for experiments below.
• Please refer to sampler.py to see explanations of some key parameters.

Parameters

aggr	arch	dataset	dropout
define how to aggregate embeddings of each node and its neighbors' embeddings ,which can be 'concat', 'mean'. The neighbors' embeddings are generated based on GCN	e.g. '1-1-0', means there're three layers, the first and the second layer employ message passing on the graph, then aggregate the embeddings of each node and its neighbors. The last layer only updates each node's embedding. The message passing mechanism comes from GCN	the name of dataset, which can be 'ppi', 'flickr', 'reddit', 'yelp', 'amazon'	the dropout of model used in train_sampling.py
edge_budget	gpu	length	log_dir
the expected number of edges in each subgraph, which is specified in the paper	-1 means cpu, otherwise 'cuda:gpu', e.g. if gpu=0, use 'cuda:0'	the length of each random walk	the directory storing logs
lr	n_epochs	n_hidden	no_batch_norm
learning rate	training epochs	hidden dimension	True if do NOT employ batch normalization in each layer
node_budget	num_subg	num_roots	sampler
the expected number of nodes in each subgraph, which is specified in the paper	the expected number of pre_sampled subgraphs	the number of roots to generate random walks	specify which sampler to use, which can be 'node', 'edge', 'rw', corresponding to node, edge, random walk sampler
use_val	val_every	num_workers_sampler	num_subg_sampler
True if use best model to test, which is stored by earlystop mechanism	validate per 'val_every' epochs	number of workers (processes) specified for internal dataloader in SAINTSampler, which is to pre-sample subgraphs	the maximal number of pre-sampled subgraphs
batch_size_sampler	num_workers
batch size of internal dataloader in SAINTSampler	number of workers (processes) specified for external dataloader in train_sampling.py, which is to sample subgraphs in training phase

Minibatch training

Run with following:

python train_sampling.py --task $task $online
# online sampling: e.g. python train_sampling.py --task ppi_n --online
# offline sampling: e.g. python train_sampling.py --task flickr_e

• $task includes ppi_n, ppi_e, ppi_rw, flickr_n, flickr_e, flickr_rw, reddit_n, reddit_e, reddit_rw, yelp_n, yelp_e, yelp_rw, amazon_n, amazon_e, amazon_rw. For example, ppi_n represents running experiments on dataset ppi with node sampler
• If $online is --online, we sample subgraphs on-the-fly in the training phase, while discarding pre-sampled subgraphs. If $online is empty, we utilize pre-sampled subgraphs in the training phase.

LICENSE

© 2022 meongju0o0 uses Apache 2.0 License. Powered by DGL Team.