实现一个最简单的从raw graph - Explainer -> subgraph -Predictor -> y 的pipeline, 其中Explainer 和Predictor 都用最基本的GCN模型就行。

实现这样一个从原始图到子图解释再到预测的简单pipeline，首先我们需要三个基本组件：数据预处理，GCNExplainer（用于提取子图的解释），以及GCNPredictor（基于子图进行预测）。这里以Python和PyTorch Geometric为例：

1. **数据预处理**:

   import torch_geometric.data as Data
   from torch_geometric.transforms import ToSparseTensor

   def preprocess_graph(raw_data):
       data = Data(data=ToSparseTensor(raw_data.edge_index),
                   x=raw_data.node_features)  # 将输入转换为稀疏张量格式
       return data

2. **GCNExplainer** (假设使用注意力机制作为基础的解释器):

   class GCNExplainer(nn.Module):
       def __init__(self, in_channels, out_channels, num_layers):
           super(GCNExplainer, self).__init__()
           self.encoder = nn.Sequential(*[gnn.GCNConv(in_channels if i==0 else out_channels, out_channels, normalize=False)
                                         for i in range(num_layers)])
           self.attention = nn.Linear(out_channels, 1)  # 注意力头

       def forward(self, data, target_node):
           h = self.encoder(data.x)
           attention_scores = self.attention(h[target_node])
           attention_scores = F.softmax(attention_scores, dim=-1).unsqueeze(0)  # 获取目标节点的注意力权重
           subgraph_x = torch.spmm(data.adj_matrix, h * attention_scores).squeeze()  # 提取子图特征
           return subgraph_x

3. **GCNPredictor** (一个简单的GCN模型用于预测):

   class GCNPredictor(nn.Module):
       def __init__(self, in_channels, hidden_channels, num_layers):
           super(GCNPredictor, self).__init__()
           self.predictor = nn.Sequential(
               gnn.GCNConv(in_channels, hidden_channels),
               nn.ReLU(),
               gnn.GCNConv(hidden_channels, 1)
           )
           
       def forward(self, subgraph_x):
           output = self.predictor(subgraph_x)
           return output.view(-1)  # 输出节点的预测值

4. **Pipeline实现**:

   def pipeline(graph, explainer, predictor, target_node):
       graph = preprocess_graph(graph)
       subgraph_x = explainer(graph, target_node)
       prediction = predictor(subgraph_x)
       return prediction