Implementing a GNN in PyTorch from scratch

The previous section focused on understanding and implementing a graph convolution operation. In this section, we’ll walk you through a basic implementation of a graph neural network to illustrate how to apply these methods to graphs if you start from scratch. If this approach appears complicated, don’t worry; GNNs are relatively complex models to implement. Thus, we’ll introduce PyTorch Geometric in a later section, which provides tools to ease the implementation of, and the data management for, graph neural networks.

Defining the NodeNetwork model

We will start this section by showing a PyTorch from-scratch implementation of a GNN. We will take a top-down approach, starting with the main neural network model, which we call NodeNetwork, and then we will fill in the individual details:

import networkx as nx
import torch
from torch.nn.parameter import Parameter
import numpy as np
import math
import torch.nn...