【论文代码】GraphSAGE（更新ing）

文章目录

一、官方代码

1.1 加载数据

1.2 Unsupervised Loss

1.3 Models

1.4 评估与模型使用

1.5 Main

二、PyG版本

class SAGEConv(MessagePassing):

Reference

一、官方代码

Cora数据集由机器学习论文组成。 这些论文分为以下七个类别之一：

基于案例

遗传算法

神经网络

概率方法

强化学习

规则学习

理论

这些论文的选择方式是，在最终语料库中，每篇论文引用或被至少一篇其他论文引用。整个语料库中有 2708篇论文。

在词干堵塞和去除词尾后，只剩下 1433个 唯一的单词。文档频率小于10的所有单词都被删除。

1.1 加载数据

1.2 Unsupervised Loss

1.3 Models

1.4 评估与模型使用

1.5 Main

二、PyG版本

x i ′ = W 1 x i + W 2 ⋅ m e a n j ∈ N ( i ) x j \mathbf{x}^{\prime}_i = \mathbf{W}_1 \mathbf{x}_i + \mathbf{W}_2 \cdot \mathrm{mean}_{j \in \mathcal{N(i)}} \mathbf{x}_j xi′ =W1 xi +W2 ⋅meanj∈N(i) xj

class SAGEConv(MessagePassing):

（1）in_channels (int or tuple): Size of each input sample, or :obj:-1 to derive the size from the first input(s) to the forward method.A tuple corresponds to the sizes of source and target dimensionalities.

（2）out_channels (int): Size of each output sample.

（3）normalize (bool, optional): If set to :obj:True, output features will be :math: ℓ 2 \ell_2 ℓ2 -normalized, i.e., :math: x i ′ ∥ x i ′ ∥ 2 \frac{\mathbf{x}^{\prime}_i} {\| \mathbf{x}^{\prime}_i \|_2} ∥xi′ ∥2 xi′ . (default: :obj:False)

（4）root_weight (bool, optional): If set to :obj:False, the layer will not add transformed root node features to the output.(default: :obj:True)

（5）bias (bool, optional): If set to :obj:False, the layer will not learn an additive bias. (default: :obj:True)

（6）**kwargs (optional): Additional arguments of

官方代码：https://github.com/williamleif/graphsage-simple/

如果我们使用pytorch的PyG也能很方便调用：

# -*- coding: utf-8 -*- """ Created on Fri Oct 8 23:16:13 2021 @author: 86493 """ import torch from torch_geometric.datasets import Planetoid from torch_geometric.transforms import NormalizeFeatures dataset = Planetoid(root='C:/dataset/Cora/processed', name='Cora', transform=NormalizeFeatures()) print() print(f'Dataset: {dataset}:') print('======================') print(f'Number of graphs: {len(dataset)}') print(f'Number of features: {dataset.num_features}') print(f'Number of classes: {dataset.num_classes}') data = dataset[0] # Get the first graph object. print() print(data) print('======================') # Gather some statistics about the graph. print(f'Number of nodes: {data.num_nodes}') print(f'Number of edges: {data.num_edges}') print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}') print(f'Number of training nodes: {data.train_mask.sum()}') print(f'Training node label rate: {int(data.train_mask.sum()) / data.num_nodes:.2f}') print(f'Contains isolated nodes: {data.has_isolated_nodes()}') print(f'Contains self-loops: {data.has_self_loops()}') print(f'Is undirected: {data.is_undirected()}') # 2.可视化节点表征分布的方法 import matplotlib.pyplot as plt from sklearn.manifold import TSNE def visualize(h, color): z = TSNE(n_components=2).fit_transform(h.detach().cpu().numpy()) plt.figure(figsize=(10,10)) plt.xticks([]) plt.yticks([]) plt.scatter(z[:, 0], z[:, 1], s=70, c=color, cmap="Set2") plt.show() # 网络的构造 import torch from torch.nn import Linear import torch.nn.functional as F """ from torch_geometric.nn import GCNConv class GCN(torch.nn.Module): def __init__(self, hidden_channels): super(GCN, self).__init__() torch.manual_seed(12345) self.conv1 = GCNConv(dataset.num_features, hidden_channels) self.conv2 = GCNConv(hidden_channels, dataset.num_classes) def forward(self, x, edge_index): x = self.conv1(x, edge_index) x = x.relu() x = F.dropout(x, p=0.5, training=self.training) x = self.conv2(x, edge_index) return x """ from torch_geometric.nn import SAGEConv class SAGE(torch.nn.Module): def __init__(self, hidden_channels): super(SAGE, self).__init__() torch.manual_seed(12345) self.conv1 = SAGEConv(dataset.num_features, hidden_channels) self.conv2 = SAGEConv(hidden_channels, dataset.num_classes) def forward(self, x, edge_index): x = self.conv1(x, edge_index) x = x.relu() x = F.dropout(x, p=0.5, training=self.training) x = self.conv2(x, edge_index) return x model = SAGE(hidden_channels=16) print(model) # 可视化由未经训练的图神经网络生成的节点表征 model = SAGE(hidden_channels=16) model.eval() out = model(data.x, data.edge_index) visualize(out, color=data.y) # 图神经网络的训练 model = SAGE(hidden_channels=16) optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4) criterion = torch.nn.CrossEntropyLoss() def train(): model.train() optimizer.zero_grad() # Clear gradients. out = model(data.x, data.edge_index) # Perform a single forward pass. loss = criterion(out[data.train_mask], data.y[data.train_mask]) # Compute the loss solely based on the training nodes. loss.backward() # Derive gradients. optimizer.step() # Update parameters based on gradients. return loss for epoch in range(1, 201): loss = train() print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}') # 增加loss折线图 import pandas as pd df = pd.DataFrame(columns = ["Loss"]) # columns列名 df.index.name = "Epoch" for epoch in range(1, 201): loss = train() #df.loc[epoch] = loss.item() df.loc[epoch] = loss.item() df.plot() # 图神经网络的测试 def test(): model.eval() out = model(data.x, data.edge_index) pred = out.argmax(dim=1) # Use the class with highest probability. test_correct = pred[data.test_mask] == data.y[data.test_mask] # Check against ground-truth labels. test_acc = int(test_correct.sum()) / int(data.test_mask.sum()) # Derive ratio of correct predictions. return test_acc test_acc = test() print(f'Test Accuracy: {test_acc:.4f}') # 可视化由训练后的图神经网络生成的节点表征 model.eval() out = model(data.x, data.edge_index) visualize(out, color=data.y)

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打印出的结果为：

Dataset: Cora(): ====================== Number of graphs: 1 Number of features: 1433 Number of classes: 7 Data( x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708] ) ====================== Number of nodes: 2708 Number of edges: 10556 Average node degree: 3.90 Number of training nodes: 140 Training node label rate: 0.05 Contains isolated nodes: False Contains self-loops: False Is undirected: True SAGE( (conv1): SAGEConv(1433, 16) (conv2): SAGEConv(16, 7) )

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可视化的图如上所示，也可以可视化loss的200个epoch的折线图：

Reference

（1）https://github.com/twjiang/graphSAGE-pytorch/tree/master/src

（2）https://zhuanlan.zhihu.com/p/410407148

（3）https://blog.csdn.net/weixin_44027006/article/details/116888648

（4）GraphSAGE 代码解析(二) - layers.py

（5）https://www.zhihu.com/search?q=GraphSAGE%E4%BB%A3%E7%A0%81PyG%E8%A7%A3%E8%AF%BB&utm_content=search_history&type=content

机器学习 神经网络

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