Datasets › OGB-LSC
OGB-LSC (OGB Large-Scale Challenge)
OGB Large-Scale Challenge (OGB-LSC) is a collection of three real-world datasets for advancing the state-of-the-art in large-scale graph ML. OGB-LSC provides graph datasets that are orders of magnitude larger than existing ones and covers three core graph learning tasks -- link prediction, graph regression, and node classification.
OGB-LSC consists of three datasets: MAG240M-LSC, WikiKG90M-LSC, and PCQM4M-LSC. Each dataset offers an independent task.
- MAG240M-LSC is a heterogeneous academic graph, and the task is to predict the subject areas of papers situated in the heterogeneous graph (node classification).
- WikiKG90M-LSC is a knowledge graph, and the task is to impute missing triplets (link prediction).
- PCQM4M-LSC is a quantum chemistry dataset, and the task is to predict an important molecular property, the HOMO-LUMO gap, of a given molecule (graph regression).
Benchmarks archive 2025-07-28
All 3 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Graph Regression | PCQM4M-LSC | Graphormer Test MAE 13.28 | Do Transformers Really Perform Bad for Graph Representation? | microsoft/Graphormer +4 | 11 | Compare |
| Knowledge Graphs | WikiKG90M-LSC | TransE-Concat Test MRR 85.48 | OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs | dmlc/dgl +5 | 4 | Compare |
| Node Classification | MAG240M-LSC | R-GraphSAGE (NS) Test Accuracy 68.94 | OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs | dmlc/dgl +5 | 4 | Compare |
Papers archive 2025-07-28
7 shown of 7 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 34. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Graph Convolutions Enrich the Self-Attention in Transformers! | 1 | 1 | 7 Dec 2023 | ran 19 of 29 samples (10 unverified) |
| Graph Propagation Transformer for Graph Representation Learning | 1 | 1 | 19 May 2023 | ran 4 of 8 samples (4 unverified; 8 pointer-only for licence) |
| O-GNN: Incorporating Ring Priors into Molecular Modeling | 1 | 1 | 1 May 2023 | not harvested |
| Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs | 2 | 1 | 27 Oct 2021 | ran 0 of 1 samples (1 unverified; 1 pointer-only for licence) |
| Global Self-Attention as a Replacement for Graph Convolution | 3 | 1 | 7 Aug 2021 | ran 0 of 4 samples (4 unverified) |
| Do Transformers Really Perform Bad for Graph Representation? | 5 | 1 | 9 Jun 2021 | not harvested |
| OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs | 6 | 13 | 17 Mar 2021 | not harvested |
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
Unknown
Modalities archive 2025-07-28
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- OGB-LSC
- MAG240M-LSC
- WikiKG90M-LSC
- PCQM4M-LSC
4 variant names, as the archive lists them.
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