graph neural architecture search

graph neural architecture search图神经网络架构搜索，是一种自动化技术，其目标是让机器自动为特定的图数据和学习任务，找到最优的图神经网络模型结构，从而取代传统的人工设计模型的过程。

可以理解为用AI来设计AI在图神经网络领域的具体应用。

GraphNAS的核心组成部分

包含三个核心组成部分：

• 搜索空间
  • 定义机器可以选择的所有可能的模型组件和连接方式
  • 组件级搜索：搜索每个GNN层的最佳操作
  • 架构级搜索：搜索模型的宏观连接结构
• 搜索策略
  • 决定如何在搜索空间中高效的寻找性能优异的架构
  • 常见策略
    • 强化学习
    • 进化算法
    • 基于梯度的方法
    • 贝叶斯优化
• 性能评估策略
  • 评估搜索出的每个候选架构的性能

示例

常见的是用于节点分类任务，这里采用强化学习作为搜索策略。

例如基于强化学习的图神经网络架构搜索：

1. 搜索空间定义

search_space = {
	'layer_type': ['gcn', 'gat', 'sage', 'gin', 'graph'],
    'hidden_dim': [64, 128, 256],
    'attention_heads': [1, 2, 4, 8],
    'aggregation': ['mean', 'max', 'sum', 'lstm'],
    'activation': ['relu', 'prelu', 'elu', 'tanh'],
    'skip_connection': [True, False],
    'dropout_rate': [0.0, 0.1, 0.3, 0.5],
    'num_layers': [2, 3, 4]  # 网络深度
  }

2. 控制器设计 RNN

import torch
import torch.nn as nn

class Controller(nn.Module):
    def __init__(self, vocab_sizes, hidden_dim=100):
        super(Controller, self).__init__()
        self.hidden_dim = hidden_dim
        self.lstm = nn.LSTMCell(hidden_dim, hidden_dim)
        
        # 为每个架构决策创建embedding和线性层
        self.embeddings = nn.ModuleList([
            nn.Embedding(vocab_size, hidden_dim) for vocab_size in vocab_sizes
        ])
        self.decoders = nn.ModuleList([
            nn.Linear(hidden_dim, vocab_size) for vocab_size in vocab_sizes
        ])
        
    def forward(self, inputs):
        hx, cx = torch.zeros(inputs.size(0), self.hidden_dim), \
                 torch.zeros(inputs.size(0), self.hidden_dim)
        
        actions = []
        log_probs = []
        
        for i, (emb, dec) in enumerate(zip(self.embeddings, self.decoders)):
            # 通过LSTM处理
            hx, cx = self.lstm(inputs, (hx, cx))
            
            # 生成动作概率
            logits = dec(hx)
            prob = F.softmax(logits, dim=-1)
            
            # 采样动作
            action = torch.multinomial(prob, 1).squeeze()
            log_prob = F.log_softmax(logits, dim=-1)
            
            actions.append(action)
            log_probs.append(log_prob.gather(1, action.unsqueeze(1)).squeeze())
            
            # 准备下一时间步的输入
            inputs = emb(action)
        
        return actions, log_probs

3. 可微分GNN架构实现

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

class SearchableGNN(nn.Module):
    def __init__(self, num_features, num_classes, architecture_decision):
        super(SearchableGNN, self).__init__()
        self.layers = nn.ModuleList()
        self.architecture = architecture_decision
        
        # 解析架构决策
        num_layers = architecture_decision['num_layers']
        layer_types = architecture_decision['layer_type']
        hidden_dims = architecture_decision['hidden_dim']
        
        # 构建GNN层
        in_dim = num_features
        for i in range(num_layers):
            layer_type = layer_types[i]
            out_dim = hidden_dims[i]
            
            if layer_type == 'gcn':
                layer = GCNConv(in_dim, out_dim)
            elif layer_type == 'gat':
                heads = architecture_decision['attention_heads'][i]
                layer = GATConv(in_dim, out_dim, heads=heads, concat=True)
                out_dim = out_dim * heads
            elif layer_type == 'sage':
                layer = SAGEConv(in_dim, out_dim)
            elif layer_type == 'gin':
                layer = GINConv(
                    nn.Sequential(
                        nn.Linear(in_dim, out_dim),
                        nn.ReLU(),
                        nn.Linear(out_dim, out_dim)
                    )
                )
            
            self.layers.append(layer)
            in_dim = out_dim
        
        # 分类层
        self.classifier = nn.Linear(in_dim, num_classes)
        self.dropout = architecture_decision['dropout_rate']
    
    def forward(self, x, edge_index):
        for i, layer in enumerate(self.layers):
            x = layer(x, edge_index)
            x = F.relu(x)  # 使用ReLU激活
            x = F.dropout(x, p=self.dropout, training=self.training)
        
        return self.classifier(x)

4. 搜索算法

class GraphNAS:
    def __init__(self, data, num_features, num_classes):
        self.data = data
        self.num_features = num_features
        self.num_classes = num_classes
        
        # 控制器参数：7个决策点
        vocab_sizes = [
            len(search_space['layer_type']),  # 层类型选择
            len(search_space['hidden_dim']),  # 隐藏维度
            len(search_space['attention_heads']),  # 注意力头数
            len(search_space['aggregation']),  # 聚合方式
            len(search_space['activation']),  # 激活函数
            len(search_space['skip_connection']),  # 跳跃连接
            len(search_space['dropout_rate'])  # dropout率
        ]
        
        self.controller = Controller(vocab_sizes)
        self.optimizer = torch.optim.Adam(self.controller.parameters(), lr=0.001)
    
    def decode_architecture(self, actions):
        """将控制器的动作解码为具体的架构参数"""
        architecture = {}
        
        architecture['layer_type'] = [search_space['layer_type'][actions[0]]]
        architecture['hidden_dim'] = [search_space['hidden_dim'][actions[1]]]
        architecture['attention_heads'] = [search_space['attention_heads'][actions[2]]]
        architecture['aggregation'] = search_space['aggregation'][actions[3]]
        architecture['activation'] = search_space['activation'][actions[4]]
        architecture['skip_connection'] = search_space['skip_connection'][actions[5]]
        architecture['dropout_rate'] = search_space['dropout_rate'][actions[6]]
        architecture['num_layers'] = 2  # 固定为2层简化示例
        
        return architecture
    
    def evaluate_architecture(self, architecture):
        """评估特定架构的性能"""
        model = SearchableGNN(self.num_features, self.num_classes, architecture)
        optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
        criterion = nn.CrossEntropyLoss()
        
        # 快速训练和验证（实际应用中会训练更多轮次）
        model.train()
        for epoch in range(50):  # 简化训练过程
            optimizer.zero_grad()
            out = model(self.data.x, self.data.edge_index)
            loss = criterion(out[self.data.train_mask], self.data.y[self.data.train_mask])
            loss.backward()
            optimizer.step()
        
        # 在验证集上评估
        model.eval()
        with torch.no_grad():
            logits = model(self.data.x, self.data.edge_index)
            pred = logits.argmax(dim=1)
            val_acc = (pred[self.data.val_mask] == self.data.y[self.data.val_mask]).float().mean()
        
        return val_acc.item()
    
    def search(self, num_episodes=100):
        """执行架构搜索"""
        best_accuracy = 0
        best_architecture = None
        
        for episode in range(num_episodes):
            # 控制器生成架构
            inputs = torch.zeros(1, self.controller.hidden_dim)
            actions, log_probs = self.controller(inputs)
            
            # 解码架构
            architecture = self.decode_architecture([a.item() for a in actions])
            
            # 评估架构
            accuracy = self.evaluate_architecture(architecture)
            
            # 强化学习更新：准确性作为奖励
            reward = accuracy
            baseline = 0.8  # 简单的基线
            advantage = reward - baseline
            
            # 计算策略梯度
            policy_loss = []
            for log_prob in log_probs:
                policy_loss.append(-log_prob * advantage)
            policy_loss = torch.stack(policy_loss).sum()
            
            # 更新控制器
            self.optimizer.zero_grad()
            policy_loss.backward()
            self.optimizer.step()
            
            # 记录最佳架构
            if accuracy > best_accuracy:
                best_accuracy = accuracy
                best_architecture = architecture
            
            print(f'Episode {episode+1}: Accuracy = {accuracy:.4f}, '
                  f'Best = {best_accuracy:.4f}')
        
        return best_architecture, best_accuracy

5. 运行搜索

# 初始化搜索
graph_nas = GraphNAS(data, num_features=1433, num_classes=7)

# 开始搜索
best_arch, best_acc = graph_nas.search(num_episodes=50)

print("搜索完成！")
print(f"最佳架构: {best_arch}")
print(f"最佳准确率: {best_acc:.4f}")

6. 搜索结构示例

经过搜索后，会发现类似这样的最有架构：

best_architecture = {
    'layer_type': ['gat', 'gcn'],  # 第一层用GAT，第二层用GCN
    'hidden_dim': [256, 128],      # 隐藏维度递减
    'attention_heads': [8, 1],     # 第一层8头注意力
    'aggregation': 'mean',
    'activation': 'elu',
    'skip_connection': True,
    'dropout_rate': 0.3,
    'num_layers': 2
}

效率上搜索过程计算大。