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AutoGL/autogl/datasets
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README.md

Datasets are derived from PyG, OGB and CogDL.

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 （*: using utils.random_splits_mask_class for splitting dataset is recommended.)
Dataset PyG CogDL x y edge_index edge_attr train/val/test node train/val/test mask
Cora ✓ ✓ ✓ ✓ ✓ ✓
Citeseer ✓ ✓ ✓ ✓ ✓ ✓
Pubmed ✓ ✓ ✓ ✓ ✓ ✓
Amazon Computers ✓ ✓ ✓ ✓ ✓
Amazon Photo ✓ ✓ ✓ ✓ ✓
Coauthor CS ✓ ✓ ✓ ✓ ✓
Coauthor Physics ✓ ✓ ✓ ✓ ✓
Reddit ✓ ✓ ✓ ✓ ✓ ✓
  • supervised graph classification: MUTAG, IMDB-B, IMDB-M, PROTEINS, COLLAB
Dataset PyG CogDL x y edge_index edge_attr train/val/test node train/val/test mask adj
Mutag ✓ ✓ ✓ ✓ ✓
IMDB-B ✓ ✓ ✓
IMDB-M ✓ ✓ ✓
PROTEINS ✓ ✓ ✓ ✓
COLLAB ✓ ✓ ✓

  • node classification datasets from OGB: ogbn-products, ogbn-proteins, ogbn-arxiv, ogbn-papers100M and ogbn-mag.

  • graph classification datasets from OGB: ogbg-molhiv, ogbg-molpcba, ogbg-ppa and ogbg-code.

TODO:
In future version, AutoGL will support the following benchmarks for different tasks:

  • unsupervised node classification: PPI, Blogcatalog, Wikipedia
  • heterogeneous node classification: DBLP, ACM, IMDB
  • link prediction: PPI, Wikipedia, Blogcatalog
  • multiplex link prediction: Amazon, YouTube, Twitter
  • link prediction datasets from OGB: ogbl-ppa, ogbl-collab, ogbl-ddi, ogbl-citation, ogbl-wikikg and ogbl-biokg.
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