Class CoraGraphDataset

python/dgl/data/citation_graph.py:340–452

r"""Cora citation network dataset. Nodes mean paper and edges mean citation relationships. Each node has a predefined feature with 1433 dimensions. The dataset is designed for the node classification task. The task is to predict the category of certain paper. Statistics

class CoraGraphDataset(CitationGraphDataset):
    r"""Cora citation network dataset.

    Nodes mean paper and edges mean citation
    relationships. Each node has a predefined
    feature with 1433 dimensions. The dataset is
    designed for the node classification task.
    The task is to predict the category of
    certain paper.

    Statistics:

    - Nodes: 2708
    - Edges: 10556
    - Number of Classes: 7
    - Label split:

        - Train: 140
        - 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 = CoraGraphDataset()
    >>> g = dataset[0]
    >>> num_class = dataset.num_classes
    >>>
    >>> # get node feature
    >>> feat = g.ndata['feat']
    >>>
    >>> # get data split