13 datasets found

PCQM4Mv2 dataset is used for graph regression tasks

The TUDataset is a collection of benchmark datasets for learning with graphs. It contains 14 datasets, each with a different type of graph.

Graph representation learning typically aims to learn an informative embedding for each graph node based on the graph topology (link) information.

Anomalous citation detection in academic citation networks

The MoleculeNet dataset is a collection of molecular property prediction tasks. It contains 17 datasets, each with a different type of molecular graph.

Heterogeneous graph convolution approach for learning node representation on heterogeneous graphs with a hybrid micro/macro level convolutional operation.

PatentDB is a Chinese patent dataset, consisting of 68275 patents and their applicants and inventors.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a first hitting diffusion model for generating manifold, graph and...

OGB is a collection of graph datasets.

PCQM4M and PCQM4Mv2 are large-scale molecular graph datasets.

Benchmarking graph neural networks.

OGB-LSC provides the three large-scale realistic benchmark datasets, covering the core graph ML tasks of node classification, link prediction, and graph regression.

Technological intellectual property data is generally composed of complex information like creative entities, resource entities, inter-entity relationships, and entity attributes.