torch_geometric.datasets.RandomPartitionGraphDataset

class RandomPartitionGraphDataset(root: str, num_classes: int, num_nodes_per_class: int, node_homophily_ratio: float, average_degree: float, num_graphs: int = 1, num_channels: Optional[int] = None, is_undirected: bool = True, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, **kwargs: Any)[source]

Bases: StochasticBlockModelDataset

The random partition graph dataset from the “How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision” paper. This is a synthetic graph of communities controlled by the node homophily and the average degree, and each community is considered as a class. The node features are sampled from normal distributions where the centers of clusters are vertices of a hypercube, as computed by the sklearn.datasets.make_classification() method.

Parameters:

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

  • num_classes (int) – The number of classes.

  • num_nodes_per_class (int) – The number of nodes per class.

  • node_homophily_ratio (float) – The degree of node homophily.

  • average_degree (float) – The average degree of the graph.

  • num_graphs (int, optional) – The number of graphs. (default: 1)

  • num_channels (int, optional) – The number of node features. If given as None, node features are not generated. (default: None)

  • is_undirected (bool, optional) – Whether the graph to generate is undirected. (default: True)

  • 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)

  • **kwargs (optional) – The keyword arguments that are passed down to sklearn.datasets.make_classification() method in drawing node features.