Graph Neural Network 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 (MNIST) 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) Usage 1. python graph_neural_network.py --model fc 2. python graph_neural_network.py --model conv 3. python graph_neural_network.py --model gaussian_graph 4. python graph_neural_network.py --model graph --pred_edge 5. python graph_neural_network.py --model graph Visualize Filters Sparse Filter Gaussian Filter
