torch_geometric.datasets.StochasticBlockModelDataset

class StochasticBlockModelDataset(root: str, block_sizes: Union[List[int], Tensor], edge_probs: Union[List[List[float]], Tensor], num_graphs: int = 1, num_channels: Optional[int] = None, is_undirected: bool = True, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, force_reload: bool = False, **kwargs: Any)[source]

Bases: InMemoryDataset

A synthetic graph dataset generated by the stochastic block model. The node features of each block 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.

  • block_sizes ([int] or LongTensor) – The sizes of blocks.

  • edge_probs ([[float]] or FloatTensor) – The density of edges going from each block to each other block. Must be symmetric if the graph is undirected.

  • 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)

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

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