基于图神经网络的节点表征学习

最新推荐文章于 2024-12-28 12:59:23 发布

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最新推荐文章于 2024-12-28 12:59:23 发布 · 162 阅读

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该博客对比了MLP和两种图神经网络（GCN和GAT）在节点分类任务上的表现。在Cora数据集上，通过训练和测试损失以及准确率，展示了GCN相对于MLP的优越性，GCN达到了更高的测试准确性。

基于图神经网络的节点表征学习

通过节点分类任务来比较MLP和GCN,GAT（两个知名度很高的图神经网络）三者的节点表征学习能力

一、加载数据

from torch_geometric.datasets import Planetoid
from torch_geometric.transforms import NormalizeFeatures
dataset = Planetoid(root='./dataset', 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.contains_isolated_nodes()}')
print(