82 datasets found

The Collective Classification in Network Data dataset is used for graph neural network research.

The Cora, Citeseer, and Polblogs datasets are widely used for graph neural network research.

Graph neural networks (GNNs) achieve remarkable performance for tasks on graph data. However, recent works show they are extremely vulnerable to adversarial structural...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used five tasks and ten graph datasets.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used five tasks and ten graph datasets.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used five tasks and ten graph datasets.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used five tasks and ten graph datasets.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used five tasks and ten graph datasets.

Graph neural networks (GNNs) are a powerful family of models that operate over graph-structured data and have achieved state-of-the-art performance on node and graph...

Graph neural networks (GNNs) have achieved great success on various tasks and fields that require relational modeling. GNNs aggregate node features using the graph structure as...

GNNExplainer: Generating Explanations for Graph Neural Networks

The FunQG framework is a novel molecular graph coarsening framework for more efficient learning of molecular representations.

Graph U-Nets for node classification and graph classification tasks

The dataset for New Zealand COVID-19 pandemic forecasting using spatio-temporal graph neural networks.

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

The dataset used in the paper is a collection of nine graphs with different degrees of homophily.

The dataset used in the paper is a comprehensive survey on graph neural networks.

The dataset used in the paper is a collection of six low-homophily node classification tasks.

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...