create a graph neural network in pytorch in python

To create a Graph Neural Network (GNN) in PyTorch, we can follow the steps below:

Define the graph structure and node/edge features.
Define the message passing function.
Define the readout function.
Define the forward function that applies message passing and readout functions to the graph.

Here is an example code snippet that demonstrates how to implement a simple GNN for node classification task using PyTorch Geometric library:


main.py
import torch
from torch_geometric.nn import MessagePassing
from torch_geometric.datasets import Planetoid
from torch_geometric.utils import add_self_loops, degree

class GNN(MessagePassing):
    def __init__(self, in_channels, out_channels):
        super(GNN, self).__init__(aggr='add')  # "Add" aggregation.
        self.lin = torch.nn.Linear(in_channels, out_channels)

    def forward(self, x, edge_index):
        # x has shape [N, in_channels]
        # edge_index has shape [2, E]

        # Step 1: Add self-loops to the adjacency matrix.
        edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))

        # Step 2: Linearly transform node feature matrix.
        x = self.lin(x)

        # Step 3: Compute normalization factor for each node.
        row, col = edge_index
        deg = degree(col, x.size(0), dtype=x.dtype)
        deg_inv_sqrt = deg.pow(-0.5)
        norm = deg_inv_sqrt[row]*deg_inv_sqrt[col]

        # Step 4-6: Start propagating messages.
        return self.propagate(edge_index, x=x, norm=norm)

    def message(self, x_j, norm):
        # x_j has shape [E, out_channels]

        # Step 4: Normalize node features.
        return norm.view(-1, 1) * x_j

    def update(self, aggr_out):
        # aggr_out has shape [N, out_channels]

        # Step 6: Return new node embeddings.
        return aggr_out

# Load the dataset
dataset = Planetoid(root='/tmp/Cora', name='Cora')

# Initialize the model and optimizer
model = GNN(dataset.num_node_features, 16)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)

# Training loop
def train():
    model.train()

    optimizer.zero_grad()
    out = model(data.x, data.edge_index)
    loss = criterion(out[data.train_mask], data.y[data.train_mask])
    loss.backward()
    optimizer.step()

    return loss.item()

for epoch in range(200):
    loss = train()
    print('Epoch {:03d}, Loss: {:.4f}'.format(epoch, loss))
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This code snippet uses the MessagePassing class from PyTorch Geometric library to perform message passing step in graph neural network. It also uses the Planetoid dataset to load Cora dataset and train the model for node classification.

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