AutoGL Dataset
We provide various common datasets based on PyTorch-Geometric
, Deep Graph Library
and OGB
.
Besides, users are able to leverage a unified abstraction provided in AutoGL, GeneralStaticGraph
, which is towards both static homogeneous graph and static heterogeneous graph.
A basic example to construct an instance of GeneralStaticGraph
is shown as follows.
from autogl.data.graph import GeneralStaticGraph, GeneralStaticGraphGenerator
''' Construct a custom homogeneous graph '''
custom_static_homogeneous_graph: GeneralStaticGraph = GeneralStaticGraphGenerator.create_homogeneous_static_graph(
{'x': torch.rand(2708, 3), 'y': torch.rand(2708, 1)}, torch.randint(0, 1024, (2, 10556))
)
''' Construct a custom heterogemneous graph '''
custom_static_heterogeneous_graph: GeneralStaticGraph = 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)),
}
)
Supporting datasets
AutoGL now supports the following benchmarks for different tasks:
Semi-supervised node classification: Cora, Citeseer, Pubmed, Amazon Computers, Amazon Photo, Coauthor CS, Coauthor Physics, Reddit, etc.
Dataset | PyG | DGL | default train/val/test split |
---|---|---|---|
Cora | ✓ | ✓ | ✓ |
Citeseer | ✓ | ✓ | ✓ |
Pubmed | ✓ | ✓ | ✓ |
Amazon Computers | ✓ | ✓ | |
Amazon Photo | ✓ | ✓ | |
Coauthor CS | ✓ | ✓ | |
Coauthor Physics | ✓ | ✓ | |
✓ | ✓ | ✓ | |
ogbn-products | ✓ | ✓ | ✓ |
ogbn-proteins | ✓ | ✓ | ✓ |
ogbn-arxiv | ✓ | ✓ | ✓ |
ogbn-papers100M | ✓ | ✓ | ✓ |
Graph classification: MUTAG, IMDB-Binary, IMDB-Multi, PROTEINS, COLLAB, etc.
Dataset | PyG | DGL | Node Feature | Label | Edge Features |
---|---|---|---|---|---|
MUTAG | ✓ | ✓ | ✓ | ✓ | ✓ |
IMDB-Binary | ✓ | ✓ | ✓ | ||
IMDB-Multi | ✓ | ✓ | ✓ | ||
PROTEINS | ✓ | ✓ | ✓ | ✓ | |
COLLAB | ✓ | ✓ | ✓ | ||
ogbg-molhiv | ✓ | ✓ | ✓ | ✓ | ✓ |
ogbg-molpcba | ✓ | ✓ | ✓ | ✓ | ✓ |
ogbg-ppa | ✓ | ✓ | ✓ | ✓ | |
ogbg-code2 | ✓ | ✓ | ✓ | ✓ | ✓ |
Link Prediction: At present, AutoGL utilizes various homogeneous graphs towards node classification to conduct automatic link prediction.
Construct custom dataset by instances of GeneralStaticGraph
The following example shows the way to compose a custom dataset by a sequence of instances of GeneralStaticGraph
.