GCN/GraphSAGE/GAT代码

• 导包

  import torch
  import torch.nn.functional as F
  from torch_geometric.nn import GCNConv, SAGEConv, GATConv
  from torch_geometric.datasets import Planetoid
  

• 导入数据集

  dataset = Planetoid(root='./tmp/Cora', name='Cora')
  print(dataset.num_node_features)	# 节点的特征数 1433
  print(dataset.num_classes)			# 节点的类别数 7
  data = dataset[0]
  print(data.y)	# 节点对应的类别 
  print(data.x)	# 节点特征矩阵  [2708, 1433]
  print(data.edge_index)  # 图的边关系	[2, 10556]
  print(data.train_mask)  # 为true的位置，代表是训练集 140
  print(data.val_mask)	# 为true的位置，代表是验证集 500
  print(data.test_mask)	# 为true的位置，代表是测试集 1000
  

1. GCN模型

• 构建

  class GCN_Net(torch.nn.Module):
      def __init__(self, features, hidden, classes):
          super(GCN_Net, self).__init__()
          self.conv1 = GCNConv(features, hidden)
          self.conv2 = GCNConv(hidden, classes)
  
      def forward(self, data):
          x, edge_index = data.x, data.edge_index
          x = self.conv1(x, edge_index) 
          x = F.relu(x)
          x = F.dropout(x, training=self.training)
          x = self.conv2(x, edge_index)
          return F.log_softmax(x, dim=1)
  

• 训练和测试

  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
  model = GCN_Net(dataset.num_node_features, 16, dataset.num_classes).to(device)
  data = dataset[0].to(device)	
  optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
  
  model.train()
  for epoch in range(200):
      optimizer.zero_grad()
      out = model(data)
      loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
      loss.backward()
      optimizer.step()
  
  model.eval()
  _, pred = model(data).max(dim=1)
  correct = pred[data.test_mask].eq(data.y[data.test_mask]).sum()
  acc = int(correct) / int(data.test_mask.sum())
  print('GCN:', acc)
  

2. GraphSAGE模型

• 构建

  class GraphSAGE_Net(torch.nn.Module):
      def __init__(self, features, hidden, classes):
          super(GraphSAGE_Net, self).__init__()
          self.sage1 = SAGEConv(features, hidden)
          self.sage2 = SAGEConv(hidden, classes)
  
      def forward(self, data):
          x, edge_index = data.x, data.edge_index
          x = self.sage1(x, edge_index)
          x = F.relu(x)
          x = F.dropout(x, training=self.training)
          x = self.sage2(x, edge_index)
          return F.log_softmax(x, dim=1)
  

• 训练和测试

  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
  model = GraphSAGE_Ne.to(device)t(dataset.num_node_features, 16, dataset.num_classes).to(device)
  data = dataset[0].to(device)
  optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
  
  model.train()
  for epoch in range(200):
      optimizer.zero_grad()
      out = model(data)
      loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
      loss.backward()
      optimizer.step()
  
  model.eval()
  _, pred = model(data).max(dim=1)
  correct = pred[data.test_mask].eq(data.y[data.test_mask]).sum()
  acc = int(correct) / int(data.test_mask.sum())
  print('GraphSAGE', acc)
  

3. GAT模型

• 构建

  class GAT_Net(torch.nn.Module):
      def __init__(self, features, hidden, classes, heads=1):
          super(GAT_Net, self).__init__()
          self.gat1 = GATConv(features, hidden, heads=heads)
          self.gat2 = GATConv(hidden * heads, classes)
  
      def forward(self, data):
          x, edge_index = data.x, data.edge_index
          x = self.gat1(x, edge_index)
          x = F.relu(x)
          x = F.dropout(x, training=self.training)
          x = self.gat2(x, edge_index)
          return F.log_softmax(x, dim=1)
  

• 训练和测试

  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
  model = GAT_Net(dataset.num_node_features, 16, dataset.num_classes, heads=4).to(device)
  data = dataset[0].to(device)
  optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
  
  model.train()
  for epoch in range(200):
      optimizer.zero_grad()
      out = model(data)
      loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
      loss.backward()
      optimizer.step()
  
  model.eval()
  _, pred = model(data).max(dim=1)
  correct = pred[data.test_mask].eq(data.y[data.test_mask]).sum()
  acc = int(correct) / int(data.test_mask.sum())
  print('GAT', acc)