SquirrelDataset(raw_dir=None, force_reload=False, verbose=True, transform=None)[source]¶
Wikipedia page-page network on squirrels from Multi-scale Attributed Node Embedding and later modified by Geom-GCN: Geometric Graph Convolutional Networks
Nodes represent articles from the English Wikipedia, edges reflect mutual links between them. Node features indicate the presence of particular nouns in the articles. The nodes were classified into 5 classes in terms of their average monthly traffic.
Number of Classes: 5
10 train/val/test splits
raw_dir (str, optional) – Raw file directory to store the processed data. Default: ~/.dgl/
force_reload (bool, optional) – Whether to re-download the data source. Default: False
verbose (bool, optional) – Whether to print progress information. Default: True
transform (callable, optional) – A transform that takes in a
DGLGraphobject and returns a transformed version. The
DGLGraphobject will be transformed before every access. Default: None
The graph does not come with edges for both directions.
>>> from dgl.data import SquirrelDataset >>> dataset = SquirrelDataset() >>> g = dataset >>> num_classes = dataset.num_classes
>>> # get node features >>> feat = g.ndata["feat"]
>>> # get data split >>> train_mask = g.ndata["train_mask"] >>> val_mask = g.ndata["val_mask"] >>> test_mask = g.ndata["test_mask"]
>>> # get labels >>> label = g.ndata['label']
Gets the data object at index.
The number of examples in the dataset.