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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.

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

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

Datasets

Unsupervised Graph Classification Dataset:

'MUTAG', 'PTC', 'IMDBBINARY'(IMDB-B), 'IMDBMULTI'(IMDB-M), 'REDDITBINARY'(RDT-B), 'REDDITMULTI5K'(RDT-M5K) 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 periods.            Default is 20.
--batch_size       int     Size of a training batch.              Default is 128.
--lr               float   Adam optimizer learning rate.          Default is 0.01.
--log_interval     int     Interval bettwen two evaluations.	  Default is 1.
Model options
--n_layers         int     Number of GIN layers.                  Default is 3.
--hid_dim          int     Dimension of hidden layers.            Default is 32.
Semi-supervised Graph Regression:
Dataset options
 --target          str     The regression Task.                   Default is 'mu'.
 --train_num       int     Number of supervised examples.         Default is 5000.
GPU options
--gpu              int     GPU index.                             Default is -1, using CPU.
Training options
--epochs           int     Number of training periods.            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 layers.            Default is 64.
--reg              int     Regularization weight.                 Default is 0.001.

How to run examples

Training and testing unsupervised model on MUTAG.

(As graphs in these datasets are quite small and sparse, moving graphs from cpu to gpu would take a longer time than training, we recommend using cpu for these datasets).

# 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 RDT-B RDT-M5K IMDB-B IMDB-M
Accuracy Reported 89.01 61.65 82.50 53.46 73.03 49.69
DGL 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'.

Target mu alpha homo
MAE Reported 0.3169 0.5444 0.0060
The authors' code 0.2411 0.5192 0.1560
DGL 0.2355 0.5483 0.1581
  • The source of QM9 Dataset has changed so there's a gap between the MAE reported in the paper and that we reprodcued.
  • See this issue for authors' response.