sxu-yaokx / MAGCN

Multiview Graph Convolutional Networks with Attention Mechanism

This repository contains the author's implementation in Tensorflow for the paper "Multiview Graph Convolutional Networks with Attention Mechanism".

Overview

The structures of MAGCN

The overall structure of MAGCN.

The visualization results

t-SNE visualization for the computed feature representations of a pre-trained model's first hidden layer on the Cora dataset: GCN (left) and our MAGCN (right). Node colors denote classes.

Dependencies

• Python (>=3.5)

• Tensorflow (>=1.12.0)

• Keras (>=2.0.9)

Implementation

Here we provide the implementation of a MAGCN layer in TensorFlow, along with a minimal execution example (on the Cora dataset). The repository is organised as follows:

• data/ contains the necessary dataset files for Cora;
• models.py contains the implementation of the MAGCN(Model);
• layers.py contains the implementation of the MultiGraphConvolution(Layer);

Finally, train.py puts all of the above together and may be used to execute a full training run on Cora.