The Long Range Graph Benchmark (LRGB) is a collection of 5 graph learning datasets that arguably require long-range reasoning to achieve strong performance in a given task. The 5 datasets in this benchmark can be used to prototype new models that can capture long range dependencies in graphs. -|---| | PascalVOC-SP| Computer Vision | Node Classification | | COCO-SP | Computer Vision | Node Classification | | PCQM-Contact | Quantum Chemistry | Link Prediction | | Peptides-func | Chemistry | Graph Classification | | Peptides-struct | Chemistry | Graph Regression |
41 PAPERS • 5 BENCHMARKS
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. 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).
31 PAPERS • 3 BENCHMARKS
OCB contains two graph datasets, Ckt-Bench-101 and Ckt-Bench-301, for representation learning over analog circuits. Ckt-Bench-101 and Ckt-Bench-301 contain graphs (DAGs) that represent analog circuits and provide their corresponding graph-level properties: DC gain (Gain), bandwidth (BW), phase margin (PM),Figure of Tasks: graph-level prediction/regression; analog circuit search (ACS). First open source benchmark for graph learning in analog circuits.
1 PAPER • NO BENCHMARKS YET
…It uses monomers as polymer graphs to predict the property of polymer density.
…It uses monomers as polymer graphs to predict the property of polymer melting temperature.
2 PAPERS • NO BENCHMARKS YET
…It consists of 10,572 compounds, with an average of 29.39 nodes and 94.09 edges in each graph.
…It uses monomers as polymer graphs to predict the property of glass transition temperature.
2 PAPERS • 1 BENCHMARK
…Based on the PubChemQC, we define a meaningful ML task of predicting DFT-calculated HOMO-LUMO energy gap of molecules given their 2D molecular graphs. Moreover, predicting the quantum chemical property only from 2D molecular graphs without their 3D equilibrium structures is also practically favorable.
15 PAPERS • 1 BENCHMARK
…It uses monomers as polymer graphs to predict the property of oxygen permeability. It has he limited size (595 polymers), which brings great challenges to the property prediction.
3 PAPERS • NO BENCHMARKS YET
Graph Neural Networks (GNNs) have gained traction across different domains such as transportation, bio-informatics, language processing, and computer vision.