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Synthetic Generation
Synthetic Generation dataset contains synthetic graph generation tasks. -
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities
Graph pooling is an essential component of the architecture for many graph-level tasks, such as graph classification and graph generation. -
ogbn-products
Graph Neural Networks (GNNs) have become a popular approach for various applications, ranging from social network analysis to modeling chemical properties of molecules. -
GraphEraser: A Novel Machine Unlearning Framework for Graph Data
GraphEraser is a novel machine unlearning framework tailored to graph data. It proposes two novel graph partition algorithms and a learning-based aggregation method to improve... -
Learning Networked Dynamical System Models with Weak Form and Graph Neural Ne...
The dataset is used to train and test the weak Latent Dynamics Model (wLDM) and its variant, the weak Graph Koopman Bilinear Form (wGKBF) model. -
Tangent Bundle Neural Networks
The dataset is used to test the performance of Tangent Bundle Neural Networks on three tasks: denoising of a tangent vector field on the torus, reconstruction from partial... -
ogbg-molhiv
ogbg-molhiv dataset is a graph classification dataset containing 41k molecules. -
RobustMat: Neural Diffusion for Street Landmark Patch Matching under Challeng...
For autonomous vehicles (AVs), visual perception techniques based on sensors like cameras play crucial roles in information acquisition and processing. In various computer... -
MoleculeNet
The MoleculeNet dataset is a collection of molecular property prediction tasks. It contains 17 datasets, each with a different type of molecular graph. -
METR-LA, PEMS-BAY, AJILE12
The dataset used for traffic forecasting and brain network analysis. -
Open Graph Benchmark
Open Graph Benchmark (OGB) dataset contains many large-scale benchmark datasets. -
Benchmark: Datasets for Machine Learning on Graphs
Benchmarking graph neural networks. -
SoGCN: Second-Order Graph Convolutional Networks
Graph Convolutional Networks (GCNs) with multi-hop aggregation is more expressive than one-hop GCN but suffers from higher model complexity. Finding the shortest aggregation... -
Mutag dataset
Mutag dataset is a benchmark dataset for graph neural networks, containing 188 cancer and 67 non-cancer cells. -
Revisiting link prediction: a data perspective
Revisiting link prediction: a data perspective. -
Mole-BERT: Rethinking pre-training graph neural networks for molecules
Mole-BERT: Rethinking pre-training graph neural networks for molecules. -
UniAug: A Universal Graph Structure Augmentor
Graph structure augmentation pipeline UniAug to leverage the increasing scale of graph data.
