Class PubmedGraphDataset

python/dgl/data/citation_graph.py:573–685

r"""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 pub

class PubmedGraphDataset(CitationGraphDataset):
    r"""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 :class:`~dgl.DGLGraph` object and returns
        a transformed version. The :class:`~dgl.DGLGraph` object will be
        transformed before every access.
    reorder : bool
        Whether to reorder the graph using :func:`~dgl.reorder_graph`. Default: False.

    Attributes
    ----------
    num_classes: int
        Number of label classes

    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