DGCNN

A PyTorch implementation of DGCNN based on AAAI 2018 paper An End-to-End Deep Learning Architecture for Graph Classification.

Requirements

• Anaconda
• PyTorch

conda install pytorch torchvision -c pytorch

• PyTorchNet

pip install git+https://github.com/pytorch/tnt.git@master

• PyTorch Geometric

pip install torch-scatter
pip install torch-sparse
pip install torch-cluster
pip install torch-spline-conv (optional)
pip install torch-geometric

Datasets

The datasets are collected from graph kernel datasets. The code will download and extract them into data directory automatically. The 10fold_idx files are collected from pytorch_DGCNN.

Usage

Train Model

python -m visdom.server -logging_level WARNING & python train.py --data_type PTC_MR --num_epochs 200
optional arguments:
--data_type                   dataset type [default value is 'DD'](choices:['DD', 'PTC_MR', 'NCI1', 'PROTEINS', 'IMDB-BINARY', 'IMDB-MULTI', 'MUTAG', 'COLLAB'])
--batch_size                  train batch size [default value is 50]
--num_epochs                  train epochs number [default value is 100]

Visdom now can be accessed by going to 127.0.0.1:8097/env/$data_type in your browser, $data_type means the dataset type which you are training.

Benchmarks

Default PyTorch Adam optimizer hyper-parameters were used without learning rate scheduling. The model was trained with 100 epochs and batch size of 50 on a NVIDIA GTX 1070 GPU.

Here is tiny difference between this code and official paper. X is defined as a concatenated matrix of vertex labels、 vertex attributes and normalized node degrees.

Dataset	MUTAG	PTC	NCI1	PROTEINS	D&D	COLLAB	IMDB-B	IMDB-M
Num. of Graphs	188	344	4,110	1,113	1,178	5,000	1,000	1,500
Num. of Classes	2	2	2	2	2	3	2	3
Node Attr. (Dim.)	8	19	38	5	90	1	1	1
Num. of Parameters	52,035	52,387	52,995	51,939	54,659	51,940	51,811	51,940
DGCNN (official)	85.83±1.66	58.59±2.47	74.44±0.47	75.54±0.94	79.37±0.94	73.76±0.49	70.03±0.86	47.83±0.85
DGCNN (ours)	81.67±9.64	59.12±11.27	75.72±3.13	72.88±3.38	68.80±5.37	70.52±2.00	71.50±4.48	46.47±5.22
Training Time	4.48s	6.77s	61.04s	21.15s	64.71s	202.65s	15.55s	21.90s

Results

The train loss、accuracy, test loss、accuracy are showed on visdom.

MUTAG

PTC

NCI1

PROTEINS

D&D

COLLAB

IMDB-B

IMDB-M