DGL Implementation of MVGRL

This DGL example implements the model proposed in the paper Contrastive Multi-View Representation Learning on Graphs.

Author's code: https://github.com/kavehhassani/mvgrl

Example Implementor

This example was implemented by Hengrui Zhang when he was an applied scientist intern at AWS Shanghai AI Lab.

Dependencies

• Python 3.7
• PyTorch 1.7.1
• dgl 0.6.0
• networkx
• scipy

Datasets

Unsupervised Graph Classification Datasets:

'MUTAG', 'PTC_MR', 'REDDIT-BINARY', 'IMDB-BINARY', 'IMDB-MULTI'.

Dataset	MUTAG	PTC_MR	RDT-B	IMDB-B	IMDB-M
# Graphs	188	344	2000	1000	1500
# Classes	2	2	2	2	3
Avg. Graph Size	17.93	14.29	429.63	19.77	13.00

• RDT-B, IMDB-B, IMDB-M are short for REDDIT-BINARY, IMDB-BINARY and IMDB-MULTI respectively.

Unsupervised Node Classification Datasets:

'Cora', 'Citeseer' and 'Pubmed'

Dataset	# Nodes	# Edges	# Classes
Cora	2,708	10,556	7
Citeseer	3,327	9,228	6
Pubmed	19,717	88,651	3

Arguments

Graph Classification:

--dataname         str     The graph dataset name.                Default is 'MUTAG'.
--gpu              int     GPU index.                             Default is -1, using cpu.
--epochs           int     Number of training periods.            Default is 200.
--patience         int     Early stopping steps.                  Default is 20.
--lr               float   Learning rate.                         Default is 0.001.
--wd               float   Weight decay.                          Default is 0.0.
--batch_size       int     Size of a training batch.              Default is 64.
--n_layers         int     Number of GNN layers.                  Default is 4.
--hid_dim          int     Embedding dimension.                   Default is 32.

Node Classification:

--dataname         str     The graph dataset name.                Default is 'cora'.
--gpu              int     GPU index.                             Default is -1, using cpu.
--epochs           int     Number of training periods.            Default is 500.
--patience         int     Early stopping steps.                  Default is 20.
--lr1              float   Learning rate of main model.           Default is 0.001.
--lr2              float   Learning rate of linear classifer.     Default is 0.01.
--wd1              float   Weight decay of main model.            Default is 0.0.
--wd2              float   Weight decay of linear classifier.     Default is 0.0.
--epsilon          float   Edge mask threshold.                   Default is 0.01.
--hid_dim          int     Embedding dimension.                   Default is 512.
--sample_size      int     Subgraph size.                         Default is 2000.

How to run examples

Graph Classification

# Enter the 'graph' directory
cd graph

# MUTAG:
python main.py --dataname MUTAG --epochs 20

# PTC_MR:
python main.py --dataname PTC_MR --epochs 32 --hid_dim 128

# REDDIT-BINARY
python main.py --dataname REDDIT-BINARY --epochs 20 --hid_dim 128

# IMDB-BINARY
python main.py --dataname IMDB-BINARY --epochs 20 --hid_dim 512 --n_layers 2

# IMDB-MULTI
python main.py --dataname IMDB-MULTI --epochs 20 --hid_dim 512 --n_layers 2

Node Classification

For semi-supervised node classification on 'Cora', 'Citeseer' and 'Pubmed', we provide two implementations:

1. full-graph training, see 'main.py', where we contrast the local and global representations of the whole graph.
2. subgraph training, see 'main_sample.py', where we contrast the local and global representations of a sampled subgraph with fixed number of nodes.

For larger graphs(e.g. Pubmed), it would be hard to calculate the graph diffusion matrix(i.e., PPR matrix), so we try to approximate it with APPNP, see function 'process_dataset_appnp' in 'node/dataset.py' for details.

# Enter the 'node' directory
cd node

# Cora with full graph
python main.py --dataname cora --gpu 0

# Cora with sampled subgraphs
python main_sample.py --dataname cora --gpu 0

# Citeseer with full graph
python main.py --dataname citeseer --wd1 0.001 --wd2 0.01 --epochs 200 --gpu 0

# Citeseer with sampled subgraphs
python main_sample.py --dataname citeseer --wd2 0.01 --gpu 0

# Pubmed with sampled subgraphs
python main_sample.py --dataname pubmed --sample_size 4000 --epochs 400 --patience 999 --gpu 0

Performance

We use the same hyper-parameter settings as stated in the original paper.

Graph classification:

Dataset	MUTAG	PTC-MR	REDDIT-B	IMDB-B	IMDB-M
Accuracy Reported	89.7	62.5	84.5	74.2	51.2
DGL	89.4	62.2	85.0	73.8	51.1

• The datasets that the authors used are slightly different from standard TUDataset (see dgl.data.GINDataset) in the nodes' features(e.g. The node features of 'MUTAG' dataset are of dimensionality 11 rather than 7")

Node classification:

Dataset	Cora	Citeseer	Pubmed
Accuracy Reported	86.8	73.3	80.1
DGL-sample	83.2	72.6	79.8
DGL-full	83.5	73.7	OOM

• We fail to reproduce the reported accuracy on 'Cora', even with the authors' code.
• The accuracy reported by the original paper is based on fixed-sized subgraph-training.