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New Zealand COVID-19 pandemic forecasting dataset
The dataset for New Zealand COVID-19 pandemic forecasting using spatio-temporal graph neural networks. -
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. -
CORA, Citeseer, Pubmed, OGB arXiv
CORA, Citeseer, Pubmed, OGB arXiv -
Nine Graphs with Different Degrees of Homophily
The dataset used in the paper is a collection of nine graphs with different degrees of homophily. -
Comprehensive Survey on Graph Neural Networks
The dataset used in the paper is a comprehensive survey on graph neural networks. -
Six Low-Homophily Node Classification Tasks
The dataset used in the paper is a collection of six low-homophily node classification tasks. -
Streaming Graph Neural Networks
Graphs are essential representations of many real-world data such as social networks. Recent years have witnessed the increasing efforts made to extend the neural network models... -
Open Graph Benchmark (OGB) - proteins dataset
Graph representation learning typically aims to learn an informative embedding for each graph node based on the graph topology (link) information. -
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. -
TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Dri...
The Temporal Occupancy Flow Graph (TOFG) is a unified environment representation that unifies the map information and vehicle trajectories in a homogeneous graph. -
Moleculenet: A Benchmark for Molecular Machine Learning
The authors used the Moleculenet dataset for their experiments. -
Geom-gcn: Geometric graph convolutional networks
The authors used the TEXAS dataset for their experiments. -
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Open Graph Benchmark: Datasets for machine learning on graphs. -
Credit Default Dataset
The dataset used in the paper is a graph dataset, where each node represents a person and each edge represents a connection between two people. The dataset is used to evaluate... -
Recidivism Dataset
The dataset used in the paper is a graph dataset, where each node represents a person and each edge represents a connection between two people. The dataset is used to evaluate... -
IMDB-Binary dataset
The IMDB-Binary dataset is a graph neural network dataset. -
Proteins dataset
The Proteins dataset is a graph neural network dataset. -
Electrostatic problem
The dataset used in the paper is an electrostatic problem with a three-dimensional, irregular geometry.