OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

Enabling effective and efficient machine learning (ML) over large-scale graph data (e.g., graphs with billions of edges) can have a huge impact on both industrial and scientific applications. However, community efforts to advance large-scale graph ML have been severely limited by the lack of a suitable public benchmark... (read more)

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Datasets


Introduced in the Paper:

OGB-LSC

Used in the Paper:

OGB

Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Node Classification MAG240M-LSC SIGN Validation Accuracy 66.64 # 5
Test Accuracy 66.09 # 5
Node Classification MAG240M-LSC GraphSAGE (NS) Validation Accuracy 67.32 # 4
Test Accuracy 66.25 # 4
Node Classification MAG240M-LSC GAT (NS) Validation Accuracy 67.71 # 3
Test Accuracy 66.63 # 3
Node Classification MAG240M-LSC R-GraphSAGE (NS) Validation Accuracy 70.21 # 2
Test Accuracy 68.94 # 2
Node Classification MAG240M-LSC R-GAT (NS) Validation Accuracy 70.48 # 1
Test Accuracy 69.49 # 1
Graph Regression PCQM4M-LSC GIN-virtual Validation MAE 0.1396 # 4
Test MAE 14.87 # 5
Graph Regression PCQM4M-LSC GCN-Virtual Validation MAE 0.151 # 3
Test MAE 15.79 # 4
Graph Regression PCQM4M-LSC MLP-fingerprint Validation MAE 0.2044 # 1
Test MAE 20.68 # 1
Graph Regression PCQM4M-LSC GCN Validation MAE 0.1684 # 2
Test MAE 18.38 # 2
Graph Regression PCQM4M-LSC GIN Test MAE 16.78 # 3
Knowledge Graphs WikiKG90M-LSC ComplEx-RoBERTa Validation MRR 0.7052 # 3
Test MRR 0.7186 # 3
Knowledge Graphs WikiKG90M-LSC TransE-Concat Validation MRR 0.8494 # 1
Test MRR 85.48 # 1
Knowledge Graphs WikiKG90M-LSC ComplEx-Concat Validation MRR 0.8425 # 2
Test MRR 0.8637 # 2
Knowledge Graphs WikiKG90M-LSC TransE-RoBERTa Validation MRR 0.6039 # 4
Test MRR 0.6288 # 4

Methods used in the Paper


METHOD TYPE
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