Image credit: Medium

Graph Convolution Network Classifier with Sparse Adjacency Matrix Prior

Please click on the ‘Code’ button displayed above to view project source code in GitHub.


Benchmarking performances of Fully Connected Network (FCN), Deep Convolutional Neural Network (CNN) & Graph Convolution Network with 3 different priors on MNIST.

Available Models

1. Fully Connected
2. Graph (with my Custom Adj Matrix - default)
3. Convolution
4. Graph (with Gaussian Adj Matrix)
5. Graph (with Trainable Adj Matrix)

Implementation of a Graph Neural Network Classifier with 3 different priors

1. Sparse Adjacency Matrix (Feng et al., 2020)
2. Gaussian Adjacency Matrix & Normalization as per (Kipf & Welling et al., ICLR 2017)
3. Trainable Adjacency Matrix (Predict Edges)


1. Python 3
2. PyTorch
3. Numpy
4. Networkx
5. Scipy


1. python --model fc
2. python --model conv
3. python --model gaussian_graph
4. python --model graph --pred_edge
5. python --model graph
Akshay Joshi
M. Sc. Data Science & Artificial Intelligence