torch_geometric.datasets.CornellTemporalHyperGraphDataset

class CornellTemporalHyperGraphDataset(root: str, name: str, split: str = 'train', setting: str = 'transductive', transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, pre_filter: Optional[Callable] = None, force_reload: bool = False)[source]

Bases: InMemoryDataset

A collection of temporal higher-order network datasets from the “Simplicial Closure and higher-order link prediction” paper. Each of the datasets is a timestamped sequence of simplices, where a simplex is a set of \(k\) nodes.

See the original datasets page for more details about individual datasets.

Parameters:

  • root (str) – Root directory where the dataset should be saved.

  • name (str) – The name of the dataset.

  • split (str, optional) – If "train", loads the training dataset. If "val", loads the validation dataset. If "test", loads the test dataset. (default: "train")

  • setting (str, optional) – If "transductive", loads the dataset for transductive training. If "inductive", loads the dataset for inductive training. (default: "transductive")

  • transform (callable, optional) – A function/transform that takes in an torch_geometric.data.Data object and returns a transformed version. The data object will be transformed before every access. (default: None)

  • pre_transform (callable, optional) – A function/transform that takes in an torch_geometric.data.Data object and returns a transformed version. The data object will be transformed before being saved to disk. (default: None)

  • pre_filter (callable, optional) – A function that takes in an torch_geometric.data.Data object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default: None)

  • force_reload (bool, optional) – Whether to re-process the dataset. (default: False)