官网三个简单Graph示例说明三种层次的应用

参考：PyG

1、图节点层次进行分类

# Load a dataset:
dataset = Planetoid(root, name="Cora")

# Create a mini-batch loader:
loader = NeighborLoader(dataset[0], num_neighbors=[25, 10])

# Create your GNN model:
class GNN(torch.nn.Module):
    def __init__(self):
         # Choose between different GNN building blocks:
        self.conv1 = GCNConv(dataset.num_features, 16)
        self.conv2 = GCNConv(16, dataset.num_classes)

    def forward(self, x, edge_index):
        x = self.conv1(x, edge_index).relu()
        return self.conv2(x, edge_index)

# Train you GNN model:
for data in loader:
    y_hat = model(data.x, data.edge_index)
    loss = criterion(y_hat, data.y)

2、图层次进行分类

# Load a dataset:
dataset = TUDataset(root, name="PROTEINS")

# Create a mini-batch loader:
loader = DataLoader(dataset, batch_size=256)

# Create your GNN model:
class GNN(torch.nn.Module):
    def __init__(self):
        self.conv1 = GATConv(dataset.num_features, 16)
        self.conv2 = GATConv(16, 16)
        self.lin = Linear(16, dataset.num_classes)

    def forward(self, x, edge_index, batch):
        x = self.conv1(x, edge_index).relu()
        x = self.conv2(x, edge_index).relu()
         # Choose between different GNN building blocks:
        x = global_mean_pool(x, batch)
        return self.lin(x)

# Train you GNN model:
for data in loader:
    y_hat = model(data.x, data.edge_index, data.batch)
    loss = criterion(y_hat, data.y)

3、边层次进行分类

# Load a dataset:
dataset = Reddit(root)

# Create a mini-batch loader:
loader = LinkNeighborLoader(dataset[0], num_neighbors=[25, 10])

# Create your GNN model:
class GNN(torch.nn.Module):
    def __init__(self):
         # Choose between different GNN building blocks:
        self.encoder = GraphSAGE(dataset.num_features, 16, num_layers=2)
        Self.decoder = InnerProductDecoder()

    def forward(self, x, edge_index, edge_label_index):
        x = self.encoder(x, edge_index)
        return self.decoder(x, edge_label_index)

# Train you GNN model:
for data in loader:
    y_hat = model(data.x, data.edge_index, data.edge_label_index)
    loss = criterion(y_hat, data.edge_label)