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- .. _dataset:
-
- 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.
-
- .. code-block:: python
-
- 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 | ✓ | ✓ | |
- +------------------+------------+-----------+--------------------------------+
- | Reddit | ✓ | ✓ | ✓ |
- +------------------+------------+-----------+--------------------------------+
- | 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``.
-
- .. code-block:: python
- from autogl.data import InMemoryDataset
- ''' Suppose the graphs is a sequence of instances of GeneralStaticGraph '''
- graphs = [ ... ]
- custom_dataset = InMemoryDataset(graphs)
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