DGL Implementation of InfoGraph

This DGL example implements the model proposed in the paper InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization.

Paper link: https://arxiv.org/abs/1908.01000

Author's code: https://github.com/fanyun-sun/InfoGraph

Dependencies

• Python 3.7
• PyTorch 1.7.1
• dgl 0.6.0

Datasets

Unsupervised Graph Classification Dataset:

'MUTAG', 'PTC', 'IMDBBINARY', 'IMDBMULTI', 'REDDITBINARY', 'REDDITMULTI5K' of dgl.data.GINDataset.

Dataset	MUTAG	PTC	RDT-B	RDT-M5K	IMDB-B	IMDB-M
# Graphs	188	344	2000	4999	1000	1500
# Classes	2	2	2	5	2	3
Avg. Graph Size	17.93	14.29	429.63	508.52	19.77	13.00

Semi-supervised Graph Regression Dataset:

QM9 dataset for graph property prediction (regression)

Dataset	# Graphs	# Regression Tasks
QM9	130,831	12

The 12 tasks are:

Keys	Description
mu	Dipole moment
alpha	Isotropic polarizability
homo	Highest occupied molecular orbital energ
lumo	Lowest unoccupied molecular orbital energy
gap	Gap between 'homo' and 'lumo'
r2	Electronic spatial extent
zpve	Zero point vibrational energy
U0	Internal energy at 0K
U	Internal energy at 298.15K
H	Enthalpy at 298.15K
G	Free energy at 298.15K
Cv	Heat capavity at 298.15K

Arguments

Unsupervised Graph Classification:

Dataset options

 --dataname         str     The graph dataset name.               Default is 'MUTAG'.

GPU options

--gpu              int     GPU index.                             Default is -1, using CPU.

Training options

--epochs           int     Number of training epochs.             Default is 20.
--batch_size       int     Size of a training batch               Default is 128.
--lr               float   Adam optimizer learning rate.          Default is 0.01.

Model options

--n_layers         int     Number of GIN layers.                  Default is 3.
--hid_dim          int     Dimension of hidden layer.             Default is 32.

Semi-supervised Graph Regression:

Dataset options

 --target          str     The regression Task                    Default is 'mu'.
 --train_num       int     Number of supevised examples.          Default is 5000.

GPU options

--gpu              int     GPU index.                             Default is -1, using CPU.

Training options

--epochs           int     Number of training epochs.             Default is 200
--batch_size       int     Size of a training batch               Default is 20.
--val_batch_size   int     Size of a validation batch.            Default is 100
--lr               float   Adam optimizer learning rate.          Default is 0.001.

Model options

--hid_dim          int     Dimension of hidden layer.             Default is 64.
--reg              int     Regularization weight                  Default is 0.001.

How to run examples

Training and testing unsupervised model on MUTAG.(We recommend using cpu)

# MUTAG:
python unsupervised.py --dataname MUTAG --n_layers 4 --hid_dim 32

Replace 'MUTAG' with dataname in [MUTAG', 'PTC', 'IMDBBINARY', 'IMDBMULTI', 'REDDITBINARY', 'REDDITMULTI5K'] if you'd like to try other datasets.

Training and testing semi-supervised model on QM9 for graph property 'mu' with gpu.

# QM9:
python semisupervised.py --gpu 0 --target mu

Replace 'mu' with other target names above

Performance

The hyperparameter setting in our implementation is identical to that reported in the paper.

Unsupervised Graph Classification:

Dataset	MUTAG	PTC	REDDIT-B	REDDIT-M	IMDB-B	IMDB-M
Accuracy Reported	89.01	61.65	82.50	53.46	73.03	49.69
This repository	89.88	63.54	88.50	56.27	72.70	50.13

• REDDIT-M dataset would take a quite long time to load and evaluate.

Semisupervised Graph Regression on QM9:

Here we only provide the results of 'mu', 'alpha', 'homo'.

We find that there's a big gap between the reported RMSE and that of our implementation. We also tried author's code and found that the gap still existed. This issue has been reported in sunfanyunn/InfoGraph#8

Target	mu	alpha	homo
RMSE Reported	0.3169	0.5444	0.0060
Author's code	0.2411	0.5192	0.1560
This repository	0.2355	0.5483	0.1581