PubmedGraphDataset¶
-
class
dgl.data.
PubmedGraphDataset
(raw_dir=None, force_reload=False, verbose=True, reverse_edge=True, transform=None, reorder=False)[source]¶ Bases:
dgl.data.citation_graph.CitationGraphDataset
Pubmed citation network dataset.
Nodes mean scientific publications and edges mean citation relationships. Each node has a predefined feature with 500 dimensions. The dataset is designed for the node classification task. The task is to predict the category of certain publication.
Statistics:
Nodes: 19717
Edges: 88651
Number of Classes: 3
Label Split:
Train: 60
Valid: 500
Test: 1000
- Parameters
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.
reverse_edge (bool) – Whether to add reverse edges in graph. Default: True.
transform (callable, optional) – A transform that takes in a
DGLGraph
object and returns a transformed version. TheDGLGraph
object will be transformed before every access.reorder (bool) – Whether to reorder the graph using
reorder_graph()
. Default: False.
Notes
The node feature is row-normalized.
Examples
>>> dataset = PubmedGraphDataset() >>> g = dataset[0] >>> num_class = dataset.num_of_class >>> >>> # get node feature >>> 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']
-
__getitem__
(idx)[source]¶ Gets the graph object
- Parameters
idx (int) – Item index, PubmedGraphDataset has only one graph object
- Returns
graph structure, node features and labels.
ndata['train_mask']
: mask for training node setndata['val_mask']
: mask for validation node setndata['test_mask']
: mask for test node setndata['feat']
: node featurendata['label']
: ground truth labels
- Return type