README.md

Implementation of GNN

PyTorch implementation of Graph Convolutional Networks (GCN) and Deep Graph CNN (DGCNN)

Reference Papers

GCN is based on the paper presented in ICLR 2017:

• Semi-supervised classification with graph convolutional networks

DGCNN is based on the paper presented in AAAI 2018:

• An End-to-End Deep Learning Architecture for Graph Classification

Requirements

• PyTorch
• PyTorch Geometric

Datasets

This implementation uses the Lab dataset which includes two classes:

• Benign
• Mirai

Results

The results of the implementation are shown in the following table:

Detector	Training Accuracy	Testing Accuracy
GCN	99.43%	99.36%
DGCN	99.56%	99.53%