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外贸网站推广,备案ip 查询网站查询系统,搜狗竞价绑定网站要求,做微商建自己的网站有用吗目录 数据处理与转换 数据表示 数据加载 数据转换 特征归一化 添加自环 随机扰动 组合转换 图神经网络层 图卷积层（GCNConv） 图注意力层（GATConv） 池化 全局池化（Global Pooling） 全局平均池…

目录

数据处理与转换

数据表示

数据加载

数据转换

特征归一化

添加自环

随机扰动

组合转换

图神经网络层

图卷积层（GCNConv）

图注意力层（GATConv）

池化

全局池化（Global Pooling）

全局平均池化

全局最大池化

全局求和池化

基于注意力的池化（Attention-based Pooling）

基于图的池化（Graph-based Pooling）

层次化池化（Hierarchical Pooling）

采样

子图采样（Subgraph Sampling）

邻域采样（Neighbor Sampling）

模型训练与评估

训练过程

测试过程

异构图处理

异构图定义

异构图卷积

图生成模型

Deep Graph Infomax (DGI)

Graph Autoencoder (GAE)

Variational Graph Autoencoder (VGAE)

PyTorch Geometric（PyG）是PyTorch的一个扩展库，专注于图神经网络（GNN）的实现。它提供了丰富的图数据处理工具、图神经网络层和模型。以下是对PyG库中常用方法的介绍

数据处理与转换

数据表示

PyG使用 torch_geometric.data.Data 类来表示图数据，包含节点特征 x 、边索引 edge_index 、边特征 edge_attr 等

## 数据处理与转换
# 1. 数据表示
import torch
from torch_geometric.data import Data# 创建一个简单的图
x = torch.tensor([[1, 2], [3, 4], [5, 6]], dtype=torch.float)  # 节点特征
edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long)  # 边索引
edge_attr = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float)  # 边特征data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr)
print(data)  # Data(x=[3, 2], edge_index=[2, 4], edge_attr=[4])

数据加载

PyG提供了 torch_geometric.data.DataLoader 类，用于批量加载图数据

# 2. 数据加载
from torch_geometric.datasets import Planetoid
from torch_geometric.loader import DataLoader# 加载Cora数据集
dataset = Planetoid(root='./data', name='Cora')
loader = DataLoader(dataset, batch_size=32, shuffle=True)print(f"节点数: {data.num_nodes}")  # 3
print(f"边数: {data.num_edges}")  # 4
print(f"特征维度: {data.num_node_features}")  # 2
print(f"类别数: {dataset.num_classes}")  # 7for batch in loader:print(batch)# DataBatch(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708],#           batch=[2708], ptr=[2])

数据转换

  • 特征归一化

NormalizeFeatures  是一个常用的转换方法，用于将节点特征归一化到单位范数（如 0, 1 或 -1, 1）

# 3. 数据转换
# 3.1 特征归一化
from torch_geometric.transforms import NormalizeFeaturesdataset = Planetoid(root='./data', name='Cora', transform=NormalizeFeatures())# 查看归一化后的特征
data = dataset[0]
print(data.x)
# tensor([[0., 0., 0.,  ..., 0., 0., 0.],
#         [0., 0., 0.,  ..., 0., 0., 0.],
#         [0., 0., 0.,  ..., 0., 0., 0.],
#         ...,
#         [0., 0., 0.,  ..., 0., 0., 0.],
#         [0., 0., 0.,  ..., 0., 0., 0.],
#         [0., 0., 0.,  ..., 0., 0., 0.]])
  • 添加自环

AddSelfLoops  是一个转换方法，用于为图中的每个节点添加自环（即每个节点连接到自己）

# 3.2 添加自环
from torch_geometric.transforms import AddSelfLoopsdataset = Planetoid(root='./data', name='Cora', transform=AddSelfLoops())# 查看添加自环后的边索引
data = dataset[0]
print(data.edge_index)
# tensor([[   0,    0,    0,  ..., 2705, 2706, 2707],
#         [ 633, 1862, 2582,  ..., 2705, 2706, 2707]])
  • 随机扰动

RandomNodeSplit  是一个转换方法，用于随机划分训练集、验证集和测试集

# 3.3 随机扰动
from torch_geometric.transforms import RandomNodeSplitdataset = Planetoid(root='./data', name='Cora', transform=RandomNodeSplit(num_splits=10))# 查看划分后的掩码
data = dataset[0]
print(data.train_mask)
# tensor([[False,  True,  True,  ..., False, False,  True],
#         [False, False,  True,  ...,  True, False, False],
#         [False,  True, False,  ..., False, False, False],
#         ...,
#         [ True,  True,  True,  ..., False, False, False],
#         [ True,  True,  True,  ..., False, False,  True],
#         [ True,  True,  True,  ...,  True, False,  True]])
print(data.val_mask)
# tensor([[False, False, False,  ..., False,  True, False],
#         [False, False, False,  ..., False, False, False],
#         [False, False, False,  ..., False, False,  True],
#         ...,
#         [False, False, False,  ...,  True,  True, False],
#         [False, False, False,  ..., False,  True, False],
#         [False, False, False,  ..., False, False, False]])
print(data.test_mask)
# tensor([[ True, False, False,  ...,  True, False, False],
#         [ True,  True, False,  ..., False,  True,  True],
#         [ True, False,  True,  ...,  True,  True, False],
#         ...,
#         [False, False, False,  ..., False, False,  True],
#         [False, False, False,  ...,  True, False, False],
#         [False, False, False,  ..., False,  True, False]])
  • 组合转换

可以将多个转换方法组合在一起，形成一个复合转换

# 3.4 组合转换
from torch_geometric.transforms import Compose, NormalizeFeatures, AddSelfLoops# 定义一个复合转换
transform = Compose([NormalizeFeatures(), AddSelfLoops()])# 创建一个数据集，并应用复合转换
dataset = Planetoid(root='./data', name='Cora', transform=transform)# 查看转换后的数据
data = dataset[0]
print(data.x)
# tensor([[0., 0., 0.,  ..., 0., 0., 0.],
#         [0., 0., 0.,  ..., 0., 0., 0.],
#         [0., 0., 0.,  ..., 0., 0., 0.],
#         ...,
#         [0., 0., 0.,  ..., 0., 0., 0.],
#         [0., 0., 0.,  ..., 0., 0., 0.],
#         [0., 0., 0.,  ..., 0., 0., 0.]])
print(data.edge_index)
# tensor([[   0,    0,    0,  ..., 2705, 2706, 2707],
#         [ 633, 1862, 2582,  ..., 2705, 2706, 2707]])

图神经网络层

图卷积层（GCNConv）

GCNConv是图卷积网络（GCN）的基本层

## 图神经网络层
# 1. 图卷积层 GCNConv
import torch
from torch_geometric.nn import GCNConvclass GCN(torch.nn.Module):def __init__(self, in_channels, out_channels):super(GCN, self).__init__()self.conv1 = GCNConv(in_channels, 16)self.conv2 = GCNConv(16, out_channels)def forward(self, x, edge_index):x = self.conv1(x, edge_index)x = torch.relu(x)x = self.conv2(x, edge_index)return xmodel = GCN(in_channels=dataset.num_features, out_channels=dataset.num_classes)
print(model)
# GCN(
#   (conv1): GCNConv(1433, 16)
#   (conv2): GCNConv(16, 7)
# )

图注意力层（GATConv）

GATConv是图注意力网络（GAT）的基本层

# 2. 图注意力层 GATConv
from torch_geometric.nn import GATConvclass GAT(torch.nn.Module):def __init__(self, in_channels, out_channels):super(GAT, self).__init__()self.conv1 = GATConv(in_channels, 8, heads=8, dropout=0.6)self.conv2 = GATConv(8 * 8, out_channels, heads=1, concat=True, dropout=0.6)def forward(self, x, edge_index):x = torch.dropout(x, p=0.6, training=self.training)x = self.conv1(x, edge_index)x = torch.relu(x)x = torch.dropout(x, p=0.6, training=self.training)x = self.conv2(x, edge_index)return xmodel = GAT(in_channels=dataset.num_features, out_channels=dataset.num_classes)
print(model)
# GAT(
#   (conv1): GATConv(1433, 8, heads=8)
#   (conv2): GATConv(64, 7, heads=1)
# )

池化

全局池化（Global Pooling）

全局池化将整个图的所有节点聚合为一个全局表示

  • 全局平均池化
## 池化
# 1. 全局池化
# 1.1 全局平均池化
from torch_geometric.nn import global_mean_pool
from torch_geometric.datasets import TUDataset
from torch_geometric.loader import DataLoader# 加载数据集
dataset = TUDataset(root='./data', name='MUTAG')
loader = DataLoader(dataset, batch_size=32, shuffle=True)# 获取一个批次的数据
for batch in loader:x = batch.xbatch_index = batch.batchglobal_mean = global_mean_pool(x, batch_index)print("Global Mean Pooling Result:", global_mean)break
# tensor([[0.7647, 0.0588, 0.1176, 0.0000, 0.0588, 0.0000, 0.0000],
#         [0.7500, 0.1250, 0.1250, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.6250, 0.1250, 0.2500, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.5217, 0.1739, 0.3043, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.5455, 0.2727, 0.1818, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.6400, 0.1200, 0.2400, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.6364, 0.0909, 0.2727, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.7857, 0.0714, 0.1429, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.8000, 0.0667, 0.1333, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.7647, 0.0588, 0.1176, 0.0000, 0.0000, 0.0000, 0.0588],
#         [0.5000, 0.1667, 0.3333, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.7692, 0.0769, 0.1538, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.7826, 0.0435, 0.1739, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.8000, 0.0500, 0.1500, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.8696, 0.0435, 0.0870, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.7273, 0.0909, 0.1818, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.6667, 0.0833, 0.2500, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.7273, 0.0909, 0.1818, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.8125, 0.0625, 0.1250, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.8235, 0.0588, 0.1176, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.6000, 0.1000, 0.2000, 0.0000, 0.0000, 0.1000, 0.0000],
#         [0.4615, 0.1538, 0.3077, 0.0000, 0.0000, 0.0769, 0.0000],
#         [0.7647, 0.0588, 0.1765, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.6000, 0.0500, 0.2000, 0.0000, 0.0000, 0.1500, 0.0000],
#         [0.6000, 0.1000, 0.2000, 0.1000, 0.0000, 0.0000, 0.0000],
#         [0.7647, 0.0588, 0.1176, 0.0000, 0.0000, 0.0588, 0.0000],
#         [0.8000, 0.0667, 0.1333, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.4615, 0.1538, 0.3077, 0.0769, 0.0000, 0.0000, 0.0000],
#         [0.8696, 0.0435, 0.0870, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.8696, 0.0435, 0.0870, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.7273, 0.0909, 0.1818, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.8421, 0.0526, 0.1053, 0.0000, 0.0000, 0.0000, 0.0000]])
  • 全局最大池化
# 1.2 全局最大池化
from torch_geometric.nn import global_max_pool# 获取一个批次的数据
for batch in loader:x = batch.xbatch_index = batch.batchglobal_max = global_max_pool(x, batch_index)print("Global Max Pooling Result:", global_max)break
  • 全局求和池化
# 3. 全局求和池化
from torch_geometric.nn import global_add_pool# 获取一个批次的数据
for batch in loader:x = batch.xbatch_index = batch.batchglobal_sum = global_add_pool(x, batch_index)print("Global Sum Pooling Result:", global_sum)break

基于注意力的池化（Attention-based Pooling）

基于注意力的池化方法通过学习节点的重要性权重来进行池化。一个常见的例子是 Set2Set 池化

# 2. 基于注意力的池化——Set2Set
from torch_geometric.nn import Set2Set
from torch_geometric.datasets import TUDataset
from torch_geometric.loader import DataLoader# 加载数据集
dataset = TUDataset(root='./data', name='MUTAG')
loader = DataLoader(dataset, batch_size=32, shuffle=True)# 定义 Set2Set 池化
set2set = Set2Set(in_channels=dataset.num_node_features, processing_steps=3)# 获取一个批次的数据
for batch in loader:x = batch.xbatch_index = batch.batchglobal_set2set = set2set(x, batch_index)print("Set2Set Pooling Result:", global_set2set)break
# Set2Set Pooling Result: tensor([[ 0.1719,  0.0986,  0.1594, -0.0438,  0.1743,  0.1663, -0.0578,  0.8464,
#           0.0492,  0.1045,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1730,  0.0987,  0.1601, -0.0420,  0.1730,  0.1658, -0.0549,  0.8733,
#           0.0405,  0.0862,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1686,  0.0919,  0.1603, -0.0525,  0.1807,  0.1707, -0.0683,  0.7540,
#           0.0466,  0.1994,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1601,  0.1165,  0.1425, -0.0525,  0.1782,  0.1602, -0.0836,  0.6232,
#           0.2237,  0.1531,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1725,  0.0987,  0.1598, -0.0428,  0.1736,  0.1660, -0.0562,  0.8611,
#           0.0444,  0.0945,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1673,  0.1060,  0.1527, -0.0473,  0.1761,  0.1642, -0.0679,  0.7570,
#           0.1187,  0.1243,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1579,  0.0996,  0.1486, -0.0658,  0.1874,  0.1695, -0.0954,  0.5284,
#           0.1662,  0.3054,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1584,  0.0969,  0.1503, -0.0665,  0.1881,  0.1709, -0.0949,  0.5327,
#           0.1503,  0.3170,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1634,  0.0976,  0.1537, -0.0581,  0.1835,  0.1695, -0.0809,  0.6464,
#           0.1135,  0.2401,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1704,  0.0952,  0.1599, -0.0479,  0.1774,  0.1684, -0.0626,  0.8042,
#           0.0466,  0.1492,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1634,  0.1081,  0.1488, -0.0522,  0.1789,  0.1640, -0.0776,  0.6743,
#           0.1595,  0.1661,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1671,  0.0980,  0.1563, -0.0518,  0.1797,  0.1682, -0.0707,  0.7332,
#           0.0855,  0.1813,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1564,  0.1193,  0.1384, -0.0562,  0.1800,  0.1590, -0.0922,  0.5527,
#           0.2663,  0.1810,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1704,  0.0952,  0.1599, -0.0479,  0.1774,  0.1684, -0.0626,  0.8042,
#           0.0466,  0.1492,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1671,  0.0980,  0.1563, -0.0518,  0.1797,  0.1682, -0.0707,  0.7332,
#           0.0855,  0.1813,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1730,  0.0987,  0.1601, -0.0420,  0.1730,  0.1658, -0.0549,  0.8733,
#           0.0405,  0.0862,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1604,  0.0972,  0.1517, -0.0631,  0.1863,  0.1704, -0.0893,  0.5779,
#           0.1356,  0.2864,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1711,  0.0985,  0.1589, -0.0451,  0.1752,  0.1666, -0.0599,  0.8281,
#           0.0550,  0.1169,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1584,  0.1178,  0.1406, -0.0542,  0.1790,  0.1597, -0.0875,  0.5910,
#           0.2432,  0.1659,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1673,  0.1060,  0.1527, -0.0473,  0.1761,  0.1642, -0.0679,  0.7570,
#           0.1187,  0.1243,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1602,  0.1097,  0.1457, -0.0562,  0.1811,  0.1638, -0.0856,  0.6077,
#           0.1926,  0.1997,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1696,  0.1047,  0.1549, -0.0444,  0.1743,  0.1641, -0.0623,  0.8062,
#           0.0945,  0.0993,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1604,  0.0972,  0.1517, -0.0631,  0.1863,  0.1704, -0.0893,  0.5779,
#           0.1356,  0.2864,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1671,  0.0980,  0.1563, -0.0518,  0.1797,  0.1682, -0.0707,  0.7332,
#           0.0855,  0.1813,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1638,  0.0919,  0.1567, -0.0605,  0.1855,  0.1723, -0.0815,  0.6416,
#           0.0853,  0.2731,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1732,  0.1049,  0.1525, -0.0508,  0.1755,  0.1624, -0.0665,  0.7700,
#           0.0553,  0.1160,  0.0000,  0.0000,  0.0586,  0.0000],
#         [ 0.1711,  0.0985,  0.1589, -0.0451,  0.1752,  0.1666, -0.0599,  0.8281,
#           0.0550,  0.1169,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1573,  0.0968,  0.1494, -0.0685,  0.1891,  0.1712, -0.0982,  0.5063,
#           0.1589,  0.3349,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1729,  0.1053,  0.1365, -0.0582,  0.1904,  0.1594, -0.0881,  0.5637,
#           0.0878,  0.1812,  0.0746,  0.0000,  0.0927,  0.0000],
#         [ 0.1586,  0.1026,  0.1477, -0.0628,  0.1855,  0.1678, -0.0924,  0.5526,
#           0.1742,  0.2733,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1646,  0.1075,  0.1500, -0.0506,  0.1781,  0.1641, -0.0746,  0.6999,
#           0.1469,  0.1533,  0.0000,  0.0000,  0.0000,  0.0000],
#         [ 0.1695,  0.0983,  0.1579, -0.0477,  0.1770,  0.1672, -0.0641,  0.7909,
#           0.0670,  0.1421,  0.0000,  0.0000,  0.0000,  0.0000]],
#        grad_fn=<CatBackward0>)

基于图的池化（Graph-based Pooling）

基于图的池化方法通过图的结构信息来进行池化。常见的方法包括 TopKPooling，通过选择重要性最高的节点来进行池化

# 3. 基于图的池化——TopKPooling
from torch_geometric.nn import TopKPooling
from torch_geometric.datasets import TUDataset
from torch_geometric.loader import DataLoader# 加载数据集
dataset = TUDataset(root='./data', name='MUTAG')
loader = DataLoader(dataset, batch_size=32, shuffle=True)# 定义 TopKPooling
pool = TopKPooling(in_channels=dataset.num_node_features, ratio=0.5) # 获取一个批次的数据
for batch in loader:x = batch.xedge_index = batch.edge_indexbatch_index = batch.batchx, edge_index, _, batch_index, _, _ = pool(x, edge_index, batch=batch_index)print("TopKPooling Result:", x)break
# tensor([[-0.0000, -0.0392, -0.0000,  ..., -0.0000, -0.0000, -0.0000],
#         [-0.0000, -0.0392, -0.0000,  ..., -0.0000, -0.0000, -0.0000],
#         [-0.0000, -0.0392, -0.0000,  ..., -0.0000, -0.0000, -0.0000],
#         ...,
#         [-0.4577, -0.0000, -0.0000,  ..., -0.0000, -0.0000, -0.0000],
#         [-0.4577, -0.0000, -0.0000,  ..., -0.0000, -0.0000, -0.0000],
#         [-0.4577, -0.0000, -0.0000,  ..., -0.0000, -0.0000, -0.0000]],
#        grad_fn=<MulBackward0>)

层次化池化（Hierarchical Pooling）

层次化池化通过多层池化操作生成图的层次化表示。一个常见的例子是 EdgePooling，通过边的合并操作来进行池化

# 4. 层次化池化——EdgePooling
from torch_geometric.nn import EdgePooling
from torch_geometric.datasets import TUDataset
from torch_geometric.loader import DataLoader# 加载数据集
dataset = TUDataset(root='./data', name='MUTAG')
loader = DataLoader(dataset, batch_size=32, shuffle=True)# 定义 EdgePooling
pool = EdgePooling(in_channels=dataset.num_node_features)  # 获取一个批次的数据
for batch in loader:x = batch.xedge_index = batch.edge_indexbatch_index = batch.batchx, edge_index, batch_index, _ = pool(x, edge_index, batch=batch_index)print("EdgePooling Result:", x)break# tensor([[0.0000, 1.5000, 1.5000,  ..., 0.0000, 0.0000, 0.0000],
#         [0.0000, 1.5000, 1.5000,  ..., 0.0000, 0.0000, 0.0000],
#         [0.0000, 1.5000, 1.5000,  ..., 0.0000, 0.0000, 0.0000],
#         ...,
#         [0.0000, 0.0000, 1.0000,  ..., 0.0000, 0.0000, 0.0000],
#         [1.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],
#         [0.0000, 0.0000, 1.0000,  ..., 0.0000, 0.0000, 0.0000]],
#        grad_fn=<MulBackward0>)

采样

子图采样（Subgraph Sampling）

子图采样是从原始图中提取一个子图，通常用于减少计算复杂度和增强模型的泛化能力

## 采样
# 1. 子图采样
import torch
from torch_geometric.data import Data
from torch_geometric.datasets import Planetoid
from torch_geometric.utils import k_hop_subgraph# 加载数据集
dataset = Planetoid(root='./data', name='Cora')
data = dataset[0]# 选择一个起始节点
start_node = 0
num_hops = 2  # 采样半径# 提取子图
sub_nodes, sub_edge_index, mapping, _ = k_hop_subgraph(start_node, num_hops, data.edge_index)# 创建子图
sub_data = Data(x=data.x[sub_nodes], edge_index=sub_edge_index, y=data.y[sub_nodes])print("Original Graph Nodes:", data.num_nodes)  # 2708
print("Subgraph Nodes:", sub_data.num_nodes)  # 8
print("Subgraph Edges:", sub_data.edge_index.shape[1])  # 20

邻域采样（Neighbor Sampling）

邻域采样通过选择节点的邻居来生成子图，适用于大规模图数据

# 2. 邻域采样
from torch_geometric.datasets import Planetoid
from torch_geometric.loader import NeighborLoader# 加载数据集
dataset = Planetoid(root='./data', name='Cora')
data = dataset[0]# 定义 NeighborSampler
loader = NeighborLoader(data,num_neighbors=[10, 10],  # 每层采样的邻居数量batch_size=1024,shuffle=True,
)# 遍历数据加载器
for batch in loader:print(batch)break

模型训练与评估

训练过程

## 模型训练与评估
# 1. 训练过程
import torch.nn.functional as Foptimizer = torch.optim.Adam(model.parameters(), lr=0.01)
criterion = torch.nn.CrossEntropyLoss()def train():model.train()optimizer.zero_grad()out = model(data.x, data.edge_index)loss = criterion(out[data.train_mask], data.y[data.train_mask])loss.backward()optimizer.step()return loss

测试过程

# 2. 测试过程
@torch.no_grad()
def test():model.eval()out = model(data.x, data.edge_index)pred = out.argmax(dim=1)correct = int(pred[data.test_mask].eq(data.y[data.test_mask]).sum().item())acc = correct / int(data.test_mask.sum())return accfor epoch in range(200):loss = train()acc = test()print(f'Epoch: {epoch + 1}, Loss: {loss:.4f}, Accuracy: {acc:.4f}')

异构图处理

异构图定义

## 异构图处理
# 1. 异构图定义
from torch_geometric.data import HeteroData
import torchdata = HeteroData()
# 添加两种类型节点
data['user'].x = torch.randn(4, 16)  # 4个用户
data['movie'].x = torch.randn(5, 32)  # 5部电影
# 添加边
data['user', 'rates', 'movie'].edge_index = torch.tensor([[0, 0, 1, 2, 3], [0, 2, 3, 1, 4]]  # user->movie评分关系
)

异构图卷积

# 2. 异构图卷积
from torch_geometric.nn import HeteroConv, GCNConv, SAGEConv
from torch_geometric.transforms import NormalizeFeaturesclass HeteroGNN(torch.nn.Module):def __init__(self, in_channels, out_channels, hidden_channels):super().__init__()self.conv1 = HeteroConv({('user', 'rates', 'movie'): SAGEConv((in_channels['user'], in_channels['movie']), hidden_channels),('movie', 'rev_rates', 'user'): GCNConv(in_channels['movie'], hidden_channels, add_self_loops=False)  # 禁用自环}, aggr='sum')self.conv2 = HeteroConv({('user', 'rates', 'movie'): SAGEConv((hidden_channels, hidden_channels), out_channels),('movie', 'rev_rates', 'user'): GCNConv(hidden_channels, out_channels, add_self_loops=False)  # 禁用自环}, aggr='sum')def forward(self, x_dict, edge_index_dict):x_dict = self.conv1(x_dict, edge_index_dict)x_dict = {key: torch.relu(x) for key, x in x_dict.items()}x_dict = self.conv2(x_dict, edge_index_dict)return x_dict# 定义输入和输出通道数
in_channels = {'user': 16, 'movie': 32}
out_channels = 7  # 假设输出通道数为7
hidden_channels = 64  # 假设隐藏层通道数为64# 实例化模型
model = HeteroGNN(in_channels, out_channels, hidden_channels)
print(model)
# HeteroGNN(
#   (conv1): HeteroConv(num_relations=2)
#   (conv2): HeteroConv(num_relations=2)
# )

图生成模型

Deep Graph Infomax (DGI)

DGI 是一种无监督图表示学习方法，通过最大化局部和全局图表示之间的一致性来学习节点嵌入

## 图生成模型
# 1. Deep Graph Infomax (DGI)
from torch_geometric.nn import DeepGraphInfomax
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import GCNConv
import torch.nn as nn
import torch.nn.functional as Fclass Encoder(nn.Module):def __init__(self, in_channels, hidden_channels):super(Encoder, self).__init__()self.conv = GCNConv(in_channels, hidden_channels)self.prelu = nn.PReLU(hidden_channels)def forward(self, x, edge_index):x = self.conv(x, edge_index)x = self.prelu(x)return xdef corruption(x, edge_index):return x[torch.randperm(x.size(0))], edge_indexdataset = Planetoid(root='./data', name='Cora')
data = dataset[0]encoder = Encoder(dataset.num_features, hidden_channels=512)
model = DeepGraphInfomax(hidden_channels=512, encoder=encoder,summary=lambda z, *args, **kwargs: torch.sigmoid(z.mean(dim=0)),corruption=corruption
)optimizer = torch.optim.Adam(model.parameters(), lr=0.01)def train():model.train()optimizer.zero_grad()pos_z, neg_z, summary = model(data.x, data.edge_index)loss = model.loss(pos_z, neg_z, summary)loss.backward()optimizer.step()return lossfor epoch in range(100):loss = train()print(f'Epoch: {epoch + 1}, Loss: {loss:.4f}')

Graph Autoencoder (GAE)

GAE 是一种基于图神经网络的自编码器，用于图生成任务。它通过学习节点嵌入来重建图的邻接矩阵

# 2. Graph Autoencoder(GAE)
from torch_geometric.nn import GCNConv
from torch_geometric.nn import GAE
import torch.nn.functional as Fclass Encoder(nn.Module):def __init__(self, in_channels, out_channels):super(Encoder, self).__init__()self.conv1 = GCNConv(in_channels, 2 * out_channels)self.conv2 = GCNConv(2 * out_channels, out_channels)def forward(self, x, edge_index):x = self.conv1(x, edge_index)x = F.relu(x)return self.conv2(x, edge_index)dataset = Planetoid(root='./data', name='Cora')
data = dataset[0]encoder = Encoder(dataset.num_features, out_channels=16)
model = GAE(encoder)optimizer = torch.optim.Adam(model.parameters(), lr=0.01)def train():model.train()optimizer.zero_grad()z = model.encode(data.x, data.edge_index)loss = model.recon_loss(z, data.edge_index)loss.backward()optimizer.step()return lossfor epoch in range(100):loss = train()print(f'Epoch: {epoch + 1}, Loss: {loss:.4f}')

Variational Graph Autoencoder (VGAE)

VGAE 是 GAE 的变体，通过引入变分推断来学习节点嵌入的分布

# 3. Variational Graph Autoencoder(VGAE)
from torch_geometric.nn import VGAE
from torch_geometric.datasets import Planetoid# 定义数据集
dataset = Planetoid(root='./data', name='Cora')
data = dataset[0]class Encoder(nn.Module):def __init__(self, in_channels, out_channels):super(Encoder, self).__init__()self.conv1 = GCNConv(in_channels, 2 * out_channels)self.conv2 = GCNConv(2 * out_channels, 2 * out_channels)def forward(self, x, edge_index):x = self.conv1(x, edge_index)x = F.relu(x)x = self.conv2(x, edge_index)mu = x[:, :x.size(1) // 2]logstd = x[:, x.size(1) // 2:]return mu, logstd# 定义 Encoder
encoder = Encoder(dataset.num_features, out_channels=16)# 定义 VGAE 模型
model = VGAE(encoder)# 定义优化器
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)# 训练函数
def train():model.train()optimizer.zero_grad()z = model.encode(data.x, data.edge_index)loss = model.recon_loss(z, data.edge_index)kl_loss = model.kl_loss()loss += kl_lossloss.backward()optimizer.step()return loss# 训练模型
for epoch in range(100):loss = train()print(f'Epoch: {epoch + 1}, Loss: {loss:.4f}')
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