【GNN】用 GCN 预测 CoraGraphDataset 结点类别

Cora Dataset 是 DGL 自带的一个论文数据集。

word_attributes 是一个维度为 1433 的词向量，词向量的每个元素对应一个词，0表示该元素对应的词不在Paper中，1表示该元素对应的词在Paper中。

class_label 是论文的类别，每篇 Paper 被映射到如下 7 个分类之一:
Case_Based、Genetic_Algorithms、Neural_Networks、Probabilistic_Methods、Reinforcement_Learning、Rule_Learning、Theory。

目标：利用 GCN，在数据集上进行训练，根据 图结构 和 图结点的特征 g.ndata['feat'] 特征字段（1433 维词向量），对 label = g.ndata['label'] （代表论文所属类别）进行预测。

查看数据集

from dgl.data import CoraGraphDataset
dataset = CoraGraphDataset()
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NumNodes: 2708 结点数量
NumEdges: 10556 边数量
NumFeats: 1433 特征维度
NumClasses: 7 类别个数
NumTrainingSamples: 140
NumValidationSamples: 500
NumTestSamples: 1000

G = dataset[0]  # dgl的获取全图的方式
G
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Graph(num_nodes=2708, num_edges=10556,
      ndata_schemes={'train_mask': Scheme(shape=(), dtype=torch.bool), 'label': Scheme(shape=(), dtype=torch.int64), 'val_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool), 'feat': Scheme(shape=(1433,), dtype=torch.float32)}
      edata_schemes={})
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可视化

import networkx as nx

nx_G = G.to_networkx().to_undirected() # 转无向图

# pos = nx.kamada_kawai_layout(nx_G)       # 适合小图的布局
pos = nx.fruchterman_reingold_layout(nx_G) # 适合于大图的布局（电子布局）

nx.draw(nx_G, pos, with_labels=True, node_color=[[.7, .7, .7]])
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GCN 模型训练

GCN.py 文件

import torch
import torch.nn as nn
from dgl.nn.pytorch import GraphConv

class GCN(nn.Module):
    def __init__(self,
                 g,
                 in_feats,
                 n_hidden,
                 n_classes,
                 n_layers,
                 activation,
                 dropout):
        super(GCN, self).__init__()
        self.g = g
        self.layers = nn.ModuleList()
        # input layer
        
        self.layers.append(GraphConv(in_feats, n_hidden, activation=activation))
        # hidden layers
        for i in range(n_layers - 1):
            self.layers.append(GraphConv(n_hidden, n_hidden, activation=activation))
            
        # output layer
        self.layers.append(GraphConv(n_hidden, n_classes))
        
        self.dropout = nn.Dropout(p=dropout)


    # 前向传播 ###############################

    def forward(self, features):
        h = features
        for i, layer in enumerate(self.layers):
            if i != 0:
                h = self.dropout(h) # 每经过一个卷积层都dropout
                                    # （除了第一个不drop）
            h = layer(self.g, h)
        return h
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main.py

import argparse
import time
import numpy as np
import torch
import torch.nn.functional as F
import dgl
from dgl.data import CoraGraphDataset, CiteseerGraphDataset, PubmedGraphDataset

from gcn import GCN          # 不使用消息传递
# from gcn_mp import GCN      # 使用消息传递

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)

if __name__ == '__main__':
   
    # 获得图数据 ###################################

    data = CoraGraphDataset()
    g = data[0]
    cuda = False

    # 获得数据集的一些特征 ##########################

    features = g.ndata['feat']
    labels = g.ndata['label']
    train_mask = g.ndata['train_mask']
    val_mask = g.ndata['val_mask']
    test_mask = g.ndata['test_mask']
    in_feats = features.shape[1]
    n_classes = data.num_labels
    n_edges = data.graph.number_of_edges()

    n_edges = g.number_of_edges()

    # 正则化 #######################################
    
    degs = g.in_degrees().float()
    norm = torch.pow(degs, -0.5)      #   1/\sqrt(degreee) 
    norm[torch.isinf(norm)] = 0

    g.ndata['norm'] = norm.unsqueeze(1) # 升一维，变成二维torch.Tensor

    ################## 设置网络参数，准备建立网络 ####################
      
    n_hidden = 16
    n_layers = 1
    dropout = 0.5         # 最后一层Graph Conv的dropout概率
    lr = 1e-2
    weight_decay = 5e-4   # L2正则化权重 (Weight for L2 loss)
    n_epochs = 200

    model = GCN(g,
                in_feats,
                n_hidden,
                n_classes,
                n_layers,
                F.relu,
                dropout)

    loss_fcn = torch.nn.CrossEntropyLoss()

    optimizer = torch.optim.Adam(model.parameters(),
                                 lr=lr,
                                 weight_decay=weight_decay)

    ################ 准备开始训练 ###################################

    # initialize graph
    dur = []
    for epoch in range(n_epochs):
        model.train()
        if epoch >= 3:
            t0 = time.time()
            
            
        # 前向计算
        logits = model(features) # logits就是最终的全连接层的输出
        
        # 计算loss
        # 因为数据集本身就是一个大图，所以没办法像传统的方法那样
        # 划分数据集，所以只能以mask列表（里面是True/False）
        # 的形式，来取得对应的预测值和真实标签list的一部分
    
        loss = loss_fcn(logits[train_mask], labels[train_mask])

        # 经典三连
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # 训练时间计时

        if epoch >= 3:
            dur.append(time.time() - t0)

        # 在测试集上评估准确率 acc
    
        acc = evaluate(model, features, labels, val_mask)
        print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
              "ETputs(KTEPS) {:.2f}". format(epoch, np.mean(dur), loss.item(),
                                             acc, n_edges / np.mean(dur) / 1000))

    # 在训练集上评估准确率 acc
    
    print()
    acc = evaluate(model, features, labels, test_mask)
    print("Test accuracy {:.2%}".format(acc))

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...
Epoch 00198 | Time(s) 0.0199 | Loss 0.3593 | Accuracy 0.7860 | ETputs(KTEPS) 529.47
Epoch 00199 | Time(s) 0.0199 | Loss 0.3676 | Accuracy 0.7840 | ETputs(KTEPS) 529.60

Test accuracy 80.00%
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