Benchmark datasets, data loaders, and evaluators for graph machine learning

A benchmark for offline goal-conditioned RL and offline RL

An open-source implementation of SEAL for link prediction in open graph benchmark (OGB) datasets.

[TPAMI 2022] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Client-side toolkit for using large language models, including where self-hosted

Generative Pre-trained Graph Eulerian Transformer [ICML2025]

The OGB-LSC is the Large Scale Competition by Open Graph Benchmark to help accelerate research into machine learning on graph structured data

Pairwise learning neural link prediction for ogb link prediction

Bags of Tricks in OGB (node classification) with GCNs.

graph learning code for ogb

CIKM 2021: Pooling Architecture Search for Graph Classification

No description available

Code of Heterogeneous interpolation on graph, which is a solution for OGB challenge

Code of the NVIDIA winning solution to the 2nd OGB-LSC at the NeurIPS 2022 challenge with dataset PCQM4Mv2

No description available

virtual node analysis on ogb benchmark dataset

No description available

Simulation code and results for https://arxiv.org/abs/1705.08527

Code for OGB competition

Final Solution for MAG240M dataset of OGB-LSC@KDDCUP2021

QuickBlox, Android

Top1 Solution on OGB Challenge (Graph Property Prediction on HIV dataset)

A DGL implementation of "DeeperGCN: All You Need to Train Deeper GCNs".

No description available

Oglaroon BBS Guides

Examples using TensorFlow GNN with Open Graph Benchmark datasets.

Final Project for the CS22W: Machine Learning with Graphs course at Stanford University (http://web.stanford.edu/class/cs224w/) in Autumn '21. We applied graph ML techniques and networks (such as the Graph Convolutional Network, GraphSAGE Network and Graph Isomorphism Network) to the ogbl-ddi dataset (https://ogb.stanford.edu/docs/linkprop/#ogbl-ddi) for drug-drug interactions.

S3GRL is a scalable SGRL method for faster link prediction using efficient precomputations.

A DGL implementation of "Combining Label Propagation and Simple Models Out-performs Graph Neural Networks" (ICLR 2021).

How to use the execution queue on Ogbon Environment