AutoGL 数据集
我们基于PyTorch-Geometric (PyG),Deep Graph Learning (DGL)及Open Graph Benchmark (OGB)等图学习库提供了多种多样的常用数据集。 同时,用户可以使用AutoGL所提供的统一静态图容器``GeneralStaticGraph``自定义静态同构图及异构图,例如:
from autogl.data.graph import GeneralStaticGraph, GeneralStaticGraphGenerator
''' 创建同构图 '''
custom_static_homogeneous_graph = GeneralStaticGraphGenerator.create_homogeneous_static_graph(
{'x': torch.rand(2708, 3), 'y': torch.rand(2708, 1)}, torch.randint(0, 1024, (2, 10556))
)
''' 创建异构图 '''
custom_static_heterogeneous_graph = GeneralStaticGraphGenerator.create_heterogeneous_static_graph(
{
'author': {'x': torch.rand(1024, 3), 'y': torch.rand(1024, 1)},
'paper': {'feat': torch.rand(2048, 10), 'z': torch.rand(2048, 13)}
},
{
('author', 'writing', 'paper'): (torch.randint(0, 1024, (2, 5120)), torch.rand(5120, 10)),
('author', 'reading', 'paper'): torch.randint(0, 1024, (2, 3840)),
}
)
提供的常用数据集
AutoGL目前提供如下多种常用基准数据集:
半监督节点分类:
数据集 | PyG | DGL | 默认train/val/test划分 |
---|---|---|---|
Cora | ✓ | ✓ | ✓ |
Citeseer | ✓ | ✓ | ✓ |
Pubmed | ✓ | ✓ | ✓ |
Amazon Computers | ✓ | ✓ | |
Amazon Photo | ✓ | ✓ | |
Coauthor CS | ✓ | ✓ | |
Coauthor Physics | ✓ | ✓ | |
✓ | ✓ | ✓ | |
ogbn-products | ✓ | ✓ | ✓ |
ogbn-proteins | ✓ | ✓ | ✓ |
ogbn-arxiv | ✓ | ✓ | ✓ |
ogbn-papers100M | ✓ | ✓ | ✓ |
图分类任务: MUTAG, IMDB-Binary, IMDB-Multi, PROTEINS, COLLAB等
数据集 | PyG | DGL | 节点特征 | 标签 | 边特征 |
---|---|---|---|---|---|
MUTAG | ✓ | ✓ | ✓ | ✓ | ✓ |
IMDB-Binary | ✓ | ✓ | ✓ | ||
IMDB-Multi | ✓ | ✓ | ✓ | ||
PROTEINS | ✓ | ✓ | ✓ | ✓ | |
COLLAB | ✓ | ✓ | ✓ | ||
ogbg-molhiv | ✓ | ✓ | ✓ | ✓ | ✓ |
ogbg-molpcba | ✓ | ✓ | ✓ | ✓ | ✓ |
ogbg-ppa | ✓ | ✓ | ✓ | ✓ | |
ogbg-code2 | ✓ | ✓ | ✓ | ✓ | ✓ |
链接预测任务:目前AutoGL可以使用针对节点分类任务的多种图数据进行自动链接预测。
通过GeneralStaticGraph序列构建自定义数据集
如下代码片段展示了通过一个由``GeneralStaticGraph``序列构建自定义数据集的方法。