PyG搭建异质图注意力网络HAN实现DBLP节点分类

目录

前言

HAN的原理请见：WWW 2019 | HAN：异质图注意力网络。

数据处理

导入数据：

path = os.path.abspath(os.path.dirname(os.getcwd()))+'\data\DBLP'
dataset = DBLP(path)
graph = dataset[0]print(graph)

输出如下：

HeteroData(
  author={
    x=[4057,334],
    y=[4057],
    train_mask=[4057],
    val_mask=[4057],
    test_mask=[4057]},
  paper={ x=[14328,4231]},
  term={ x=[7723,50]},
  conference={ num_nodes=20},(author, to, paper)={ edge_index=[2,19645]},(paper, to, author)={ edge_index=[2,19645]},(paper, to, term)={ edge_index=[2,85810]},(paper, to, conference)={ edge_index=[2,14328]},(term, to, paper)={ edge_index=[2,85810]},(conference, to, paper)={ edge_index=[2,14328]})

可以发现，DBLP数据集中有作者(author)、论文(paper)、术语(term)以及会议(conference)四种类型的节点。DBLP中包含14328篇论文(paper)， 4057位作者(author)， 20个会议(conference)， 7723个术语(term)。作者分为四个领域：数据库、数据挖掘、机器学习、信息检索。

任务：对author节点进行分类，一共4类。

由于conference节点没有特征，因此需要预先设置特征：

graph['conference'].x = torch.ones((graph['conference'].num_nodes,1))

所有conference节点的特征都初始化为

[1]

。

获取一些有用的数据：

num_classes = torch.max(graph['author'].y).item()+1
train_mask, val_mask, test_mask = graph['author'].train_mask, graph['author'].val_mask, graph['author'].test_mask
y = graph['author'].y

模型搭建

首先导入包：

from torch_geometric.nn import HANConv

模型参数：

1. in_channels：输入通道，比如节点分类中表示每个节点的特征数，一般设置为-1。
2. out_channels：输出通道，最后一层GCNConv的输出通道为节点类别数（节点分类）。
3. heads：多头注意力机制中的头数。值得注意的是，GANConv和GATConv不一样的地方在于，GANConv模型是把多头注意力的结果直接展平，而不是进行concat操作。
4. negative_slope：LeakyRELU的参数。

于是模型搭建如下：

classHAN(nn.Module):def__init__(self, in_channels, hidden_channels, out_channels):super(HAN, self).__init__()# H, D = self.heads, self.out_channels // self.heads
        self.conv1 = HANConv(in_channels, hidden_channels, graph.metadata(), heads=8)
        self.conv2 = HANConv(hidden_channels, out_channels, graph.metadata(), heads=4)defforward(self, data):
        x_dict, edge_index_dict = data.x_dict, data.edge_index_dict
        x = self.conv1(x_dict, edge_index_dict)
        x = self.conv2(x, edge_index_dict)
        x = F.softmax(x['author'], dim=1)return x

输出一下模型：

model = HAN(-1,64, num_classes).to(device)
HAN((conv1): HANConv(64, heads=8)(conv2): HANConv(4, heads=4))

1. 前向传播

查看官方文档中HANConv的输入输出要求：

可以发现，HANConv中需要输入的是节点特征字典

x_dict

和邻接关系字典

edge_index_dict

。

因此有：

x_dict, edge_index_dict = data.x_dict, data.edge_index_dict
x = self.conv1(x_dict, edge_index_dict)

此时我们不妨输出一下

x['author']

及其size：

tensor([[0.0000,0.0000,0.0000,...,0.0000,0.0000,0.0000],[0.0969,0.0601,0.0000,...,0.0000,0.0000,0.0251],[0.0000,0.0000,0.0000,...,0.1288,0.0000,0.0602],...,[0.0000,0.0000,0.0000,...,0.0096,0.0000,0.0240],[0.0000,0.0000,0.0000,...,0.0096,0.0000,0.0240],[0.0801,0.0558,0.0837,...,0.0277,0.0347,0.0000]],
       device='cuda:0', grad_fn=<SumBackward1>)

torch.Size([4057,64])

此时的x一共4057行，每一行表示一个author节点经过第一层卷积更新后的状态向量。

那么同理，由于：

x = self.conv2(x, edge_index_dict)

所以经过第二层卷积后得到的

x['author']

的size应该为：

torch.Size([4057,4])

即每个author节点的维度为4的状态向量。

由于我们需要进行4分类，所以最后需要加上一个softmax：

x = F.softmax(x, dim=1)

dim=1表示对每一行进行运算，最终每一行之和加起来为1，也就表示了该节点为每一类的概率。输出此时的x：

tensor([[0.2591,0.2539,0.2435,0.2435],[0.3747,0.2067,0.2029,0.2157],[0.2986,0.2338,0.2338,0.2338],...,[0.2740,0.2453,0.2403,0.2403],[0.2740,0.2453,0.2403,0.2403],[0.3414,0.2195,0.2195,0.2195]], device='cuda:0',
       grad_fn=<SoftmaxBackward0>)

2. 反向传播

在训练时，我们首先利用前向传播计算出输出：

f = model(graph)

f即为最终得到的每个节点的4个概率值，但在实际训练中，我们只需要计算出训练集的损失，所以损失函数这样写：

loss = loss_function(f[train_mask], y[train_mask])

然后计算梯度，反向更新！

3. 训练

训练时返回验证集上表现最优的模型：

deftrain():
    model = HAN(-1,64, num_classes).to(device)print(model)
    optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=1e-4)
    loss_function = torch.nn.CrossEntropyLoss().to(device)
    min_epochs =5
    best_val_acc =0
    final_best_acc =0
    model.train()for epoch inrange(100):
        f = model(graph)
        loss = loss_function(f[train_mask], y[train_mask])
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()# validation
        val_acc, val_loss = test(model, val_mask)
        test_acc, test_loss = test(model, test_mask)if epoch +1> min_epochs and val_acc > best_val_acc:
            best_val_acc = val_acc
            final_best_acc = test_acc
        print('Epoch {:3d} train_loss {:.5f} val_acc {:.3f} test_acc {:.3f}'.format(epoch, loss.item(), val_acc, test_acc))return final_best_acc

4. 测试

deftest(model, mask):
    model.eval()with torch.no_grad():
        out = model(graph)
        loss_function = torch.nn.CrossEntropyLoss().to(device)
        loss = loss_function(out[mask], y[mask])
    _, pred = out.max(dim=1)
    correct =int(pred[mask].eq(y[mask]).sum().item())
    acc = correct /int(test_mask.sum())return acc, loss.item()

实验结果

数据集采用DBLP网络，训练100轮，分类正确率为78.54%：

HAN Accuracy:0.7853853239177156

完整代码

代码地址：GNNs-for-Node-Classification。原创不易，下载时请给个follow和star！感谢！！