torch_geometric.datasets.BA2MotifDataset
- class BA2MotifDataset(root: str, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, force_reload: bool = False)[source]
Bases:
InMemoryDataset
The synthetic BA-2motifs graph classification dataset for evaluating explainabilty algorithms, as described in the “Parameterized Explainer for Graph Neural Network” paper.
BA2MotifDataset
contains 1000 random Barabasi-Albert (BA) graphs. Half of the graphs are attached with aHouseMotif
, and the rest are attached with a five-nodeCycleMotif
. The graphs are assigned to one of the two classes according to the type of attached motifs.This dataset is pre-computed from the official implementation. If you want to create own variations of it, you can make use of the
ExplainerDataset
:import torch from torch_geometric.datasets import ExplainerDataset from torch_geometric.datasets.graph_generator import BAGraph from torch_geometric.datasets.motif_generator import HouseMotif from torch_geometric.datasets.motif_generator import CycleMotif dataset1 = ExplainerDataset( graph_generator=BAGraph(num_nodes=25, num_edges=1), motif_generator=HouseMotif(), num_motifs=1, num_graphs=500, ) dataset2 = ExplainerDataset( graph_generator=BAGraph(num_nodes=25, num_edges=1), motif_generator=CycleMotif(5), num_motifs=1, num_graphs=500, ) dataset = torch.utils.data.ConcatDataset([dataset1, dataset2])
- Parameters:
root (str) – Root directory where the dataset should be saved.
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
)
STATS:
#graphs
#nodes
#edges
#features
#classes
1000
25
~51.0
10
2