Deep Graph Convolutional Neural Network implement by tensorflow

1 - About

GNN is a novel and powerful deep neural network for graph classification, It usually consists of (1)graph convolution layer which extract local substructure features for individual links and (2) a SortPooling layer which aggregates node-level features into a graph-level feature vector. It's directly accepts graph data as input without the need of first transforming graphs into tensors, make end-to-end gradient-based training possible. And it enables learning from global topology by sorting the vertex features instead of summing them up, which is supportd by SortPooling layer.

This repository provides a reference implementation of GNN based on Tensorflow.

For more information, please refer to:

M. Zhang, Z. Cui, M. Neumann, and Y. Chen, An End-to-End Deep Learning Architecture for Graph Classification, Proc. AAAI Conference on Artificial Intelligence (AAAI-18). and the origal PyTorch implementation of DGCNN is here

2 - Basic Usage

2.1 - Example

To run the GNN on the mutag(default setting), type the following command on the home directory:

python main.py

2.2 - Options

• python main.py --data proteins to run GNN on proteins
• python main.py --epoch 200 to assign the number of epochs, default value is 100
• python main.py -r 0.00001 or python main.py --learning_rate 0.00001 to set the learning rate which determine the speed of update.
• ...

you can check out the other options available to use python main.py --help

3 - Result

Dataset	Mutag	NCI1	PROTEINS	D&D
Nodes(max)	28	111	620	5748
Nodes(avg.)	17.93	29.87	39.06	284.32
Nodes(min)	10	3	4	30
Graphs	188	4110	1113	1178
GNN	0.8684(0.058844)	0.7073(0.018595)	0.7509(0.027505)	0.7432(0.047040)

To be continued...