图卷积神经网络GCN系列二：节点分类（含示例及代码）

图上的机器学习任务通常有三种类型：整图分类、节点分类和链接预测。本篇博客要实现的例子是节点分类，具体来说是用GCN对Cora数据集里的样本进行分类。

Cora数据集介绍：

Cora数据集由许多机器学习领域的paper构成，这些paper被分为7个类别：

Case_Based
Genetic_Algorithms
Neural_Networks
Probabilistic_Methods
Reinforcement_Learning
Rule_Learning
Theory

在该数据集中，每一篇论文至少引用了该数据集里面另外一篇论文或者被另外一篇论文所引用，数据集总共有2708篇papers。

在消除停词以及除去文档频率小于10的词汇，最终词汇表中有1433个词汇，所以特征是1433维。0和1描述的是每个单词在paper中是否存在。

把每一篇论文作为一个节点，根据论文之间的引用关系可以构建一个graph，包含2708个节点。0~139为训练节点数据，140~539为验证节点数据，1708~2707为测试节点数据。

代码：（摘自https://github.com/rexrex9/gnn/blob/main/gcn.py。讲解视频https://www.bilibili.com/video/BV1W5411N78Y?from=search&seid=6220646189261474464&spm_id_from=333.337.0.0）

import torch
import torch.nn as nn
import torch.nn.functional as F
from dgl.nn.pytorch import GraphConv
from dgl.data import CoraGraphDataset
 
class GCN( nn.Module ):
    def __init__(self,
                 g, #DGL的图对象
                 in_feats, #输入特征的维度
                 n_hidden, #隐层的特征维度
                 n_classes, #类别数
                 n_layers, #网络层数
                 activation, #激活函数
                 dropout #dropout系数
                 ):
        super( GCN, self ).__init__()
        self.g = g
        self.layers = nn.ModuleList()
        # 输入层
        self.layers.append( GraphConv( in_feats, n_hidden, activation = activation ))
        # 隐层
        for i in range(n_layers - 1):
            self.layers.append(GraphConv(n_hidden, n_hidden, activation = activation ))
        # 输出层
        self.layers.append( GraphConv( n_hidden, n_classes ) )
        self.dropout = nn.Dropout(p = dropout)
 
    def forward( self, features ):
        h = features
        for i, layer in enumerate( self.layers ):
            if i != 0:
                h = self.dropout( h )
            h = layer( self.g, h )
        return h
 
def evaluate(model, features, labels, mask):
    model.eval()
    with torch.no_grad():
        logits = model(features)
        logits = logits[mask]
        labels = labels[mask]
        _, indices = torch.max(logits, dim=1)
        correct = torch.sum(indices == labels)
        return correct.item() * 1.0 / len(labels)
 
def train(n_epochs=100, lr=1e-2, weight_decay=5e-4, n_hidden=16, n_layers=1, activation=F.relu , dropout=0.5):
    data = CoraGraphDataset()
    g=data[0]   # 图的所有信息，包含2078个节点，每个节点1433维，所有节点可分为7类。10556条边。
    features = g.ndata['feat']
    labels = g.ndata['label']
    train_mask = g.ndata['train_mask']  # 0~139为训练节点
    val_mask = g.ndata['val_mask']      # 140~539为验证节点
    test_mask = g.ndata['test_mask']    # 1708-2707为测试节点
    in_feats = features.shape[1]
    n_classes = data.num_classes
 
    model = GCN(g,
                in_feats,
                n_hidden,
                n_classes,
                n_layers,
                activation,
                dropout)
 
    loss_fcn = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam( model.parameters(),
                                 lr = lr,
                                 weight_decay = weight_decay)
    best_val_acc = 0
    for epoch in range( n_epochs ):
        model.train()
        logits = model( features )
        loss = loss_fcn( logits[ train_mask ], labels[ train_mask ] )
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        acc = evaluate(model, features, labels, val_mask)
        print("Epoch {} | Loss {:.4f} | Accuracy {:.4f} ".format(epoch, loss.item(), acc ))
        if acc > best_val_acc:
            best_val_acc = acc
            torch.save(model.state_dict(), 'models/best_model.pth')
 
    model.load_state_dict(torch.load("models/best_model.pth"))
    acc = evaluate(model, features, labels, test_mask)
    print("Test accuracy {:.2%}".format(acc))
 
if __name__ == '__main__':
    train()