引言
图基础模型（Graph Foundation Model, GFM）是近年来图机器学习领域的重要发展方向，旨在通过大规模图数据的预训练，构建能够跨领域泛化的通用图表示学习模型。与语言模型、视觉模型的基础模型范式类似，图基础模型试图解决图神经网络（GNN）在实际应用中面临的挑战：
- 标签稀疏性：大多数图数据缺乏足够的标注
- 领域迁移困难：在一个领域训练的GNN难以直接应用于其他领域
- 计算效率：大规模图的训练成本高昂
- 异质性：不同领域的图结构差异显著
图基础模型定义
图基础模型（Graph Foundation Model）是指在大规模、多样化的图数据上进行预训练的神经网络，能够通过微调或提示学习的方式，快速适应下游图任务。
class GraphFoundationModel:
"""
图基础模型接口定义
核心特性：
1. 预训练：在大规模图数据上学习通用图表示
2. 迁移：通过微调/提示适应下游任务
3. 泛化：跨领域、跨任务的能力
"""
def __init__(self, backbone: GNNArchitecture):
self.backbone = backbone
self.pretrain_objectives = []
self.prompt_strategy = None
def pretrain(self, graphs: List[Graph]):
"""多图预训练"""
for objective in self.pretrain_objectives:
for graph in graphs:
loss = objective(graph)
loss.backward()
def finetune(self, task_graph: Graph, labels: Labels):
"""任务微调"""
pass
def prompt(self, task_graph: Graph, task_description: str):
"""提示学习"""
pass1. 图预训练的挑战
1.1 图数据的异质性
与自然语言和图像不同，图数据具有高度的异质性：
| 异质性维度 | 表现 | 示例 |
|---|---|---|
| 结构异质 | 节点度数分布、聚类系数差异大 | 社交网络 vs 分子图 |
| 特征异质 | 节点/边特征维度、类型不同 | 蛋白质特征 vs 引用网络 |
| 语义异质 | 任务目标、标签含义不同 | 节点分类 vs 链接预测 |
| 规模异质 | 节点数、边数差异巨大 | 万级 vs 十亿级节点 |
1.2 预训练的风险
# 预训练-微调差异导致的负迁移风险
class NegativeTransferRisk:
"""
预训练图模型可能在以下情况下产生负迁移：
"""
risks = {
"structural_mismatch": {
"description": "预训练和微调图的拓扑结构差异大",
"symptom": "微调性能显著低于从头训练",
"mitigation": "图增强、结构正则化"
},
"feature_distribution_shift": {
"description": "节点特征分布随时间变化",
"symptom": "时序图上的性能下降",
"mitigation": "持续预训练、领域适应"
},
"task_interference": {
"description": "多任务预训练导致任务间干扰",
"symptom": "某些下游任务性能下降",
"mitigation": "任务解耦、提示学习"
},
"overfitting_to_pretraining": {
"description": "预训练目标与下游任务不一致",
"symptom": "预训练损失低但微调效果差",
"mitigation": "设计任务对齐的预训练目标"
}
}2. 图自监督预训练方法
2.1 代理任务分类
图预训练的核心是设计有效的自监督学习（SSL）目标：
图预训练代理任务
│
├─ 节点级代理
│ ├─ 上下文预测
│ │ ├─ 属性掩码（Attribute Masking）
│ │ ├─ 上下文预测（Context Prediction）
│ │ └─ 邻居对比（N Contrast）
│ │
│ └─ 特征重构
│ ├─ 自编码器（Graph Autoencoder）
│ └─ 对比学习（GraphCL等）
│
├─ 图级代理
│ ├─ 图级表示对比
│ │ ├─ 对比学习（InfoGraph, SUBG-CON）
│ │ └─ 知识蒸馏
│ │
│ └─ 图生成
│ ├─ 节点/边预测
│ └─ 图重建
│
└─ 跨层次代理
├─ 局部-全局一致性
└─ 多尺度对比
2.2 属性掩码（Attribute Masking）
节点/边属性掩码
class AttributeMaskingPretrain:
"""
属性掩码预训练
核心思想：随机掩码节点或边的属性，让模型学习恢复
"""
def __init__(self, mask_ratio=0.15):
self.mask_ratio = mask_ratio
def pretrain_step(self, graph: Graph, model: GNN):
"""一步预训练"""
# 1. 掩码节点属性
masked_graph, mask = self.mask_node_features(graph)
# 2. 前向传播
node_repr = model(masked_graph)
# 3. 预测被掩码的属性
masked_nodes = torch.where(mask)[0]
pred_features = self.predict_features(node_repr[masked_nodes])
true_features = graph.x[masked_nodes]
# 4. 计算损失
loss = F.mse_loss(pred_features, true_features)
return loss
def mask_node_features(self, graph: Graph):
"""掩码节点特征"""
num_nodes = graph.num_nodes
num_mask = int(num_nodes * self.mask_ratio)
# 随机选择要掩码的节点
mask_idx = torch.randperm(num_nodes)[:num_mask]
mask = torch.zeros(num_nodes, dtype=torch.bool)
mask[mask_idx] = True
# 保存原始特征并掩码
original_x = graph.x.clone()
graph.x[mask_idx] = 0 # 或用特殊掩码token
return graph, mask
# 边属性掩码
class EdgeAttributeMasking:
"""边属性掩码：掩码边的类型或权重"""
def pretrain_step(self, graph: Graph, model: GNN):
# 掩码边属性
num_edges = graph.edge_index.shape[1]
mask_idx = torch.randperm(num_edges)[:int(num_edges * 0.15)]
original_edge_attr = graph.edge_attr.clone()
graph.edge_attr[mask_idx] = 0
# 模型前向
node_repr = model(graph)
# 预测边属性
edge_repr = self.edge_representation(node_repr, graph.edge_index)
pred_edge_attr = self.edge_predictor(edge_repr[mask_idx])
loss = F.cross_entropy(pred_edge_attr, original_edge_attr[mask_idx])
return loss掩码策略对比
| 策略 | 描述 | 适用场景 |
|---|---|---|
| 随机掩码 | 随机选择节点/边进行掩码 | 通用 |
| 结构感知掩码 | 基于度数、重要性选择 | 异质图 |
| 属性感知掩码 | 优先掩码罕见属性 | 特征丰富的图 |
| 图级掩码 | 掩码整个子图 | 图级任务 |
2.3 图对比学习
InfoGraph: 局部-全局对比
class InfoGraphPretrain:
"""
InfoGraph: 最大化局部表示与全局表示的互信息
论文: InfoGraph: Unsupervised and Semi-supupervised Graph-Level Representation Learning via Mutual Information Maximization
"""
def __init__(self, temperature=0.5):
self.temperature = temperature
self.discriminator = MLPProjector()
def pretrain(self, graphs: List[Graph], model: GNN):
"""InfoGraph预训练"""
total_loss = 0
for graph in graphs:
# 1. 获取局部表示（节点级）
local_repr = model(graph) # [num_nodes, hidden_dim]
# 2. 获取全局表示（图级）
graph_repr = readout(local_repr) # [1, hidden_dim]
# 3. 对比损失：拉近正样本，排斥负样本
pos_score = self.calculate_positive_score(local_repr, graph_repr)
neg_score = self.calculate_negative_score(local_repr, graph_repr)
# 4. InfoNCE损失
loss = self.info_nce(pos_score, neg_score)
total_loss += loss
return total_loss / len(graphs)
def info_nce(self, pos_score, neg_scores):
"""InfoNCE损失"""
pos_exp = torch.exp(pos_score / self.temperature)
neg_exp = torch.sum(torch.exp(neg_scores / self.temperature), dim=-1)
return -torch.log(pos_exp / (pos_exp + neg_exp + 1e-8))GraphCL: 图增强对比
class GraphCLPretrain:
"""
GraphCL: 图对比学习的系统研究
核心思想：通过对图进行不同的增强，构造对比学习的正负样本
"""
def __init__(self):
self.augmentations = [
NodeDropout(),
EdgePerturbation(ratio=0.1),
AttributeMasking(ratio=0.1),
SubgraphSampling(ratio=0.5),
]
def pretrain(self, graph: Graph, model: GNN):
# 1. 构造两个增强视图
aug1 = self.random_augment(graph)
aug2 = self.random_augment(graph)
# 2. 获取表示
repr1 = model(self.aug(aug1))
repr2 = model(self.aug(aug2))
# 3. 对比损失
loss = self.contrastive_loss(repr1, repr2)
return loss
def random_augment(self, graph: Graph) -> Graph:
"""随机选择一种增强方式"""
aug = random.choice(self.augmentations)
return aug.apply(graph)
class NodeDropout:
"""节点丢弃增强"""
def __init__(self, p=0.2):
self.p = p
def apply(self, graph: Graph) -> Graph:
num_nodes = graph.num_nodes
keep_idx = torch.rand(num_nodes) > self.p
keep_idx = keep_idx.nonzero().squeeze()
# 重新索引
new_index_map = -torch.ones(num_nodes, dtype=torch.long)
new_index_map[keep_idx] = torch.arange(len(keep_idx))
# 更新边
new_edge_index = new_index_map[graph.edge_index]
valid_edges = (new_edge_index[0] >= 0) & (new_edge_index[1] >= 0)
return Graph(
x=graph.x[keep_idx],
edge_index=new_edge_index[:, valid_edges],
edge_attr=graph.edge_attr[valid_edges] if graph.edge_attr else None
)
class SubgraphSampling:
"""子图采样增强"""
def __init__(self, ratio=0.5):
self.ratio = ratio
def apply(self, graph: Graph) -> Graph:
# 随机游走采样子图
start_nodes = torch.randint(0, graph.num_nodes, (int(graph.num_nodes * self.ratio),))
sub_nodes = self.random_walk_subgraph(graph, start_nodes)
return self.extract_subgraph(graph, sub_nodes)2.4 上下文预测（Context Prediction）
class ContextPredictionPretrain:
"""
上下文预测预训练
让模型预测一个节点属于哪个子图（上下文）
来自: Hu et al. "Pre-training Graph Neural Networks"
"""
def __init__(self, k_hop=2, num_contexts=50):
self.k_hop = k_hop
self.num_contexts = num_contexts
self.context_encoder = GNN()
def pretrain(self, graph: Graph, model: GNN):
# 1. 采样锚点节点
anchor_nodes = self.sample_anchor_nodes(graph)
# 2. 获取锚点表示
node_repr = model(graph)
anchor_repr = node_repr[anchor_nodes]
# 3. 构建上下文样本
positive_contexts, negative_contexts = self.sample_contexts(
graph, anchor_nodes
)
# 4. 编码上下文
pos_context_repr = self.context_encoder(positive_contexts)
neg_context_repr = self.context_encoder(negative_contexts)
# 5. 上下文预测损失
pos_score = torch.sum(anchor_repr * pos_context_repr, dim=-1)
neg_score = torch.sum(anchor_repr * neg_context_repr, dim=-1)
loss = self.contrastive_loss(pos_score, neg_score)
return loss
def sample_contexts(self, graph, anchor_nodes):
"""采样正负上下文"""
positive = []
negative = []
for node in anchor_nodes:
# 正样本：k-hop邻居子图
pos_subgraph = self.extract_khop_subgraph(graph, node, self.k_hop)
positive.append(pos_subgraph)
# 负样本：随机子图
neg_node = torch.randint(0, graph.num_nodes, (1,)).item()
neg_subgraph = self.extract_khop_subgraph(graph, neg_node, self.k_hop)
negative.append(neg_subgraph)
return positive, negative3. 图基础模型架构
3.1 通用图骨干网络
class UniversalGraphBackbone(nn.Module):
"""
通用图骨干网络
设计原则：
1. 架构无关：支持多种GNN变体
2. 尺度无关：可处理不同规模的图
3. 异质性无关：通过参数化处理不同特征
"""
def __init__(
self,
input_dim: int,
hidden_dim: int,
num_layers: int,
gnn_type: str = "GAT", # GCN, GAT, GraphSAINT, GPS
dropout: float = 0.1,
):
super().__init__()
# 输入投影
self.input_proj = nn.Linear(input_dim, hidden_dim)
# GNN层
self.gnn_layers = nn.ModuleList([
self._create_gnn_layer(gnn_type, hidden_dim)
for _ in range(num_layers)
])
# 层归一化
self.norms = nn.ModuleList([
nn.LayerNorm(hidden_dim)
for _ in range(num_layers)
])
self.dropout = nn.Dropout(dropout)
def _create_gnn_layer(self, gnn_type, hidden_dim):
"""创建指定类型的GNN层"""
if gnn_type == "GCN":
return GCNConv(hidden_dim, hidden_dim)
elif gnn_type == "GAT":
return GATConv(hidden_dim, hidden_dim // 8, heads=8)
elif gnn_type == "GraphSAINT":
return SAGEConv(hidden_dim, hidden_dim)
elif gnn_type == "GPS":
return GPSLayer(hidden_dim, num_heads=8)
else:
raise ValueError(f"Unknown GNN type: {gnn_type}")
def forward(self, graph: Graph) -> Tensor:
x = self.input_proj(graph.x)
for gnn, norm in zip(self.gnn_layers, self.norms):
h = gnn(x, graph.edge_index)
h = norm(h)
h = F.relu(h)
h = self.dropout(h)
# 残差连接
x = x + h
return x3.2 异质图Transformer
class HeterogeneousGraphTransformer(nn.Module):
"""
异质图Transformer
处理具有多种节点类型和边类型的图
"""
def __init__(
self,
num_node_types: int,
num_edge_types: int,
hidden_dim: int,
num_layers: int,
):
super().__init__()
# 节点类型嵌入
self.node_type_embedding = nn.Embedding(num_node_types, hidden_dim)
# 边类型嵌入
self.edge_type_embedding = nn.Embedding(num_edge_types, hidden_dim)
# 异质注意力
self.layers = nn.ModuleList([
HeterophilicAttentionLayer(hidden_dim)
for _ in range(num_layers)
])
def forward(self, graph: HeteroGraph) -> Dict[str, Tensor]:
# 初始化节点表示
h = {
ntype: graph.x[ntype] + self.node_type_embedding(
torch.full((graph.x[ntype].shape[0],),
self.type_id_map[ntype])
)
for ntype in graph.node_types
}
# 多层Transformer
for layer in self.layers:
h = layer(h, graph.edge_index_dict)
return h
class HeterophilicAttentionLayer(nn.Module):
"""异质感知注意力层"""
def __init__(self, hidden_dim):
super().__init__()
self.query_proj = nn.Linear(hidden_dim, hidden_dim)
self.key_proj = nn.Linear(hidden_dim, hidden_dim)
self.value_proj = nn.Linear(hidden_dim, hidden_dim)
# 边类型特定的投影
self.edge_proj = nn.Linear(hidden_dim, hidden_dim)
def forward(self, h_dict, edge_index_dict):
"""处理异质边"""
new_h = {}
for (src_type, rel_type, dst_type), edge_index in edge_index_dict.items():
src_h = h_dict[src_type]
dst_h = h_dict[dst_type]
# 注意力计算
q = self.query_proj(dst_h)
k = self.key_proj(src_h)
v = self.value_proj(src_h)
# 边类型信息
edge_h = self.edge_proj(
self.edge_type_embedding(
torch.full((edge_index.shape[1],),
self.edge_type_id_map[rel_type])
)
)
# 注意力分数
attn = (q[edge_index[1]] * (k[edge_index[0]] + edge_h)).sum(-1)
attn = F.softmax(attn, dim=0)
# 聚合
new_h[dst_type] = (attn.unsqueeze(-1) * (v[edge_index[0]] + edge_h)).sum(dim=0)
return new_h4. 跨领域迁移学习
4.1 迁移策略
class GraphTransferLearning:
"""
图迁移学习策略
"""
strategies = {
"full_finetune": {
"description": "全量微调所有参数",
"pros": ["适应性强", "性能最优"],
"cons": ["计算成本高", "容易过拟合"],
"适用": "标注数据充足"
},
"linear_probe": {
"description": "冻结骨干网络，只微调分类头",
"pros": ["高效", "不易过拟合"],
"cons": ["表达能力受限"],
"适用": "标注数据有限"
},
"adapter": {
"description": "添加适配器模块",
"pros": ["参数高效", "可多任务"],
"cons": ["需要设计适配器"],
"适用": "多任务场景"
},
"prompt": {
"description": "图提示学习",
"pros": ["无需微调", "任务灵活"],
"cons": ["需要设计提示"],
"适用": "零样本场景"
}
}4.2 图适配器（GraphAdapter）
class GraphAdapter(nn.Module):
"""
图适配器模块
在预训练模型基础上添加少量可学习参数
"""
def __init__(self, hidden_dim, adapter_dim=64):
super().__init__()
# 下投影 + 非线性 + 上投影
self.down_proj = nn.Linear(hidden_dim, adapter_dim)
self.activation = nn.GELU()
self.up_proj = nn.Linear(adapter_dim, hidden_dim)
# 残差缩放
self.scale = nn.Parameter(torch.ones(1) * 0.1)
def forward(self, x):
return x + self.scale * self.up_proj(self.activation(self.down_proj(x)))
class AdapterGNN(nn.Module):
"""带适配器的GNN"""
def __init__(self, base_gnn: GNN, adapter_dim=64):
super().__init__()
self.base_gnn = base_gnn
# 每层后添加适配器
self.adapters = nn.ModuleList([
GraphAdapter(base_gnn.hidden_dim, adapter_dim)
for _ in range(base_gnn.num_layers)
])
def forward(self, graph):
h = self.base_gnn.input_proj(graph.x)
for gnn, adapter, norm in zip(
self.base_gnn.gnn_layers,
self.adapters,
self.base_gnn.norms
):
h = gnn(h, graph.edge_index)
h = norm(h)
h = adapter(h) # 应用适配器
h = F.relu(h)
return h
# 训练时：只更新适配器和分类头
def train_with_adapter(model, graph, labels):
optimizer = torch.optim.Adam([
{"params": model.adapters.parameters(), "lr": 1e-3},
{"params": model.classifier.parameters(), "lr": 1e-3},
# 可选：解冻部分GNN层
{"params": model.base_gnn.gnn_layers[-2:], "lr": 1e-4},
])
# 冻结基础GNN参数
for param in model.base_gnn.input_proj.parameters():
param.requires_grad = False
for param in model.base_gnn.gnn_layers[:-2].parameters():
param.requires_grad = False4.3 图提示学习（Graph Prompt Learning）
class GraphPromptLearning:
"""
图提示学习
通过设计图特定的提示，使预训练模型适应下游任务
"""
def __init__(self, base_model: GNN):
self.base_model = base_model
self.prompt_tokens = nn.Parameter(torch.randn(10, base_model.hidden_dim))
def prompt(self, graph: Graph, task_type: str) -> Graph:
"""
应用提示
Args:
graph: 输入图
task_type: "node", "edge", "graph"
"""
# 获取节点表示
node_repr = self.base_model(graph)
# 添加任务特定的提示
if task_type == "node":
# 节点级任务：添加节点提示
graph.prompt_h = node_repr + self.prompt_tokens[:5]
elif task_type == "graph":
# 图级任务：添加图级提示
graph_repr = self.readout(node_repr)
graph.prompt_h = graph_repr + self.prompt_tokens[-5:]
return graph
def predict(self, prompted_graph):
"""基于提示进行预测"""
return self.classifier(prompted_graph.prompt_h)
# GraphPrompt设计
class GraphPromptPool:
"""图提示池：学习多个可组合的提示"""
def __init__(self, num_prompts=10, prompt_dim=64, hidden_dim=512):
# 提示池
self.prompt_embeddings = nn.Parameter(
torch.randn(num_prompts, prompt_dim)
)
# 投影层
self.prompt_proj = nn.Linear(prompt_dim, hidden_dim)
def get_prompt(self, task_id):
"""获取指定任务的提示"""
return self.prompt_proj(self.prompt_embeddings[task_id])
def compose_prompts(self, task_ids):
"""组合多个提示"""
prompts = self.prompt_embeddings[task_ids]
return self.prompt_proj(prompts.mean(dim=0))5. 大规模图预训练实践
5.1 图采样策略
class GraphSamplingPretraining:
"""
大规模图采样预训练
核心思想：通过采样处理大规模图，构造mini-batch进行训练
"""
def __init__(self, model: GNN, sampler: Sampler):
self.model = model
self.sampler = sampler
def pretrain(self, large_graph: LargeGraph, num_epochs=100):
"""大规模图预训练"""
for epoch in range(num_epochs):
# 1. 采样子图batch
subgraphs = self.sampler.sample(large_graph, batch_size=32)
# 2. 在子图上计算预训练损失
total_loss = 0
for subgraph in subgraphs:
loss = self.compute_pretrain_loss(subgraph)
total_loss += loss
# 3. 反向传播
total_loss.backward()
self.optimizer.step()
self.optimizer.zero_grad()
def compute_pretrain_loss(self, subgraph):
"""计算预训练损失"""
# 使用掩码特征重建
masked_subgraph = self.mask_features(subgraph)
node_repr = self.model(masked_subgraph)
# 预测被掩码的特征
pred = self.decoder(node_repr[masked_subgraph.mask])
target = subgraph.x[masked_subgraph.mask]
return F.mse_loss(pred, target)
class GraphSAINTSampler:
"""GraphSAINT采样器"""
def __init__(self, sample_coverage=0.6):
self.sample_coverage = sample_coverage
def sample(self, graph, batch_size):
"""GraphSAINT采样"""
# 1. 计算节点采样概率
node_probs = self.compute_sampling_probability(graph)
# 2. 采样节点
sampled_nodes = torch.multinomial(node_probs, batch_size, replacement=True)
# 3. 构建子图
subgraphs = []
for nodes in sampled_nodes.split(batch_size // 4): # 分成多个子图
subgraph = self.extract_subgraph(graph, nodes)
subgraph.normalization = self.compute_subgraph_norm(
graph, nodes, node_probs
)
subgraphs.append(subgraph)
return subgraphs
def compute_sampling_probability(self, graph):
"""计算节点采样概率（基于度数）"""
degrees = degree(graph.edge_index[0], graph.num_nodes)
probs = degrees.float()
probs = probs / probs.sum()
return probs5.2 分布式图预训练
class DistributedGraphPretraining:
"""
分布式图预训练
"""
def __init__(self, num_workers=4):
self.num_workers = num_workers
self.workers = []
# 图分区器
self.partitioner = GraphPartitioner()
# 参数服务器
self.param_server = ParameterServer()
def pretrain(self, large_graph: LargeGraph):
"""分布式预训练"""
# 1. 图分区
partitions = self.partitioner.partition(large_graph, self.num_workers)
# 2. 启动工作进程
for i, partition in enumerate(partitions):
worker = GraphPretrainWorker(
worker_id=i,
local_graph=partition,
param_server=self.param_server
)
self.workers.append(worker)
worker.start()
# 3. 同步训练
for epoch in range(num_epochs):
# 各worker本地计算梯度
for worker in self.workers:
worker.compute_gradients()
# 参数服务器聚合梯度
self.param_server.aggregate_gradients()
# 更新参数
self.param_server.update_params()
# 广播新参数
self.param_server.broadcast_params()
class GraphPartitioner:
"""图分区器"""
def partition(self, graph, num_parts):
"""METIS风格图分区"""
# 使用图分区算法（如METIS、 Chaco）
partition_ids = metis_partition(graph, k=num_parts)
partitions = [[] for _ in range(num_parts)]
for node_id, part_id in enumerate(partition_ids):
partitions[part_id].append(node_id)
return partitions6. 图基础模型评估
6.1 基准数据集
class GraphBenchmark:
"""图学习基准"""
benchmarks = {
# 生物医疗
"OGBG-Mol": {
"description": "OGB分子性质预测",
"tasks": ["node", "edge", "graph"],
"scale": "medium"
},
"PCQM4Mv2": {
"description": "量子化学性质预测",
"num_nodes": "3.8M",
"task": "link prediction"
},
# 网络分析
"WikiCS": {
"description": "计算机科学论文分类",
"num_nodes": "11K",
"task": "node classification"
},
"ArXiv": {
"description": "学术引用网络",
"num_nodes": "169K",
"task": "node classification"
},
# 推荐系统
"Amazon-Coauthor": {
"description": "电商合著者网络",
"task": "node classification"
},
# 代码理解
"CodeXGLUE": {
"description": "代码图理解",
"task": "graph classification"
}
}6.2 评估指标
class GraphModelEvaluator:
"""图模型评估器"""
metrics = {
"node_classification": ["Accuracy", "F1", "AUC-ROC", "Precision", "Recall"],
"graph_classification": ["Accuracy", "F1", "ROC-AUC", "AP"],
"link_prediction": ["AUC-ROC", "Hits@K", "MRR"],
"edge_classification": ["Accuracy", "F1"]
}
def evaluate(self, model, test_data, metric="Accuracy"):
"""全面评估"""
model.eval()
if test_data.task_type == "node":
preds = model.predict_node(test_data.graph)
targets = test_data.labels
elif test_data.task_type == "graph":
preds = model.predict_graph(test_data.graphs)
targets = test_data.labels
else:
preds = model.predict_link(test_data.graph)
targets = test_data.labels
return self.compute_metrics(preds, targets, metric)
def cross_domain_evaluation(self, model, source_data, target_data):
"""跨领域评估"""
# 源域评估
source_metrics = self.evaluate(model, source_data)
# 目标域评估（零样本/微调后）
target_metrics_zero = self.evaluate(model, target_data) # 零样本
target_metrics_finetune = self.evaluate_with_finetune(
model, source_data, target_data
) # 微调后
return {
"source": source_metrics,
"target_zero_shot": target_metrics_zero,
"target_finetuned": target_metrics_finetune,
"transfer_gain": target_metrics_finetune - target_metrics_zero
}7. 应用场景
7.1 分子性质预测
class MolecularPropertyPrediction:
"""
分子性质预测：图基础模型的典型应用
预训练策略：
1. 属性掩码：掩码原子类型、电荷等
2. 上下文预测：预测化学子结构
3. 图级预测：预测分子指纹
"""
def __init__(self, pretrained_gnn):
self.model = pretrained_gnn
self.property_predictor = MLP(hidden_dim * 2, num_properties)
def predict(self, molecule_graph):
"""预测分子性质"""
# 获取分子表示
mol_repr = self.model(molecule_graph)
# 预测性质
properties = self.property_predictor(mol_repr)
return properties
# 预训练+微调流程
def molecular_pretrain_finetune():
# 1. 大规模分子图预训练
molecular_graphs = load_molecular_dataset(num_graphs=1000000)
pretrain_model = GraphFoundationModel(
backbone=GNN(hidden_dim=512, num_layers=8),
objectives=[
AttributeMasking(mask_ratio=0.15),
ContextPrediction(k_hop=2),
GraphContrastiveLoss(temperature=0.5)
]
)
pretrain_model.pretrain(molecular_graphs)
# 2. 保存预训练权重
save_checkpoint(pretrain_model, "molecular_gnn_pretrain.pt")
# 3. 微调到具体性质预测任务
task_graphs = load_qm9_dataset() # QM9数据集
finetune_model = MolecularPropertyPrediction(pretrain_model.backbone)
for epoch in range(100):
for batch in task_graphs:
loss = finetune_model.train_step(batch)
loss.backward()
optimizer.step()7.2 代码理解
class CodeUnderstanding:
"""
代码理解：AST图表示学习
"""
def pretrain_on_code(self, code_graphs):
"""代码图预训练"""
pretrain_objectives = [
# 1. 掩码节点类型
AttributeMasking(
attribute_types=["token_type", "data_type", "scope_level"]
),
# 2. 掩码AST边类型
EdgeAttributeMasking(
attribute_types=["edge_type"] # parent, child, next_sibling等
),
# 3. 数据流上下文预测
ContextPrediction(
subgraph_extractor="data_flow"
)
]
return pretrain_objectives7.3 推荐系统
class GraphBasedRecommendation:
"""
图推荐系统：用户-物品交互建模
"""
def pretrain_on_recommendation(self, interaction_graphs):
"""推荐图预训练"""
pretrain_objectives = [
# 1. 掩码节点特征
AttributeMasking(
attributes=["user_features", "item_features"]
),
# 2. 链接预测（交互预测）
LinkPredictionContrastive(),
# 3. 图对比学习
GraphCL(augmentations=[
NodeDropout(p=0.1),
EdgePerturbation(ratio=0.05),
])
]
return pretrain_objectives8. 总结与展望
8.1 当前进展
| 方向 | 代表工作 | 核心贡献 |
|---|---|---|
| 预训练目标 | GPT-GNN, GraphCL, InfoGraph | 设计有效的SSL目标 |
| 架构设计 | Hu et al.预训练框架 | 通用GNN骨干 |
| 迁移学习 | GraphPrompt, Adapter | 参数高效迁移 |
| 大规模 | GLEM, Open Graph Benchmark | 亿级节点处理 |
| 异质图 | HAN, HGT | 异质图Transformer |
8.2 未来方向
- 更大规模的预训练：探索十亿级节点的图预训练
- 更通用的表示：设计真正的”图GPT”
- 提示学习：类比LLM的提示工程
- 多模态图：结合文本、图像的异构多模态图
- 动态图：时序图、演化图的预训练