Class LINE

easygraph/functions/graph_embedding/line.py:20–240

Graph embedding via LINE. Parameters ---------- G : easygraph.Graph or easygraph.DiGraph dimension: int walk_length: int walk_num: int negative: int batch_size: int init_alpha: float order: int Returns ------- embedding_vector : dict The

class LINE(nn.Module):
    """Graph embedding via LINE.
    Parameters
    ----------
    G : easygraph.Graph or easygraph.DiGraph
    dimension: int
    walk_length: int

    walk_num: int

    negative: int
    batch_size: int

    init_alpha: float
    order: int
    Returns
    -------
    embedding_vector : dict
        The embedding vector of each node
    Examples
    --------
    >>> model = LINE(
    ...          dimension=128,
    ...          walk_length=80,
    ...          walk_num=20,
    ...          negative=5,
    ...          batch_size=128,
    ...          init_alpha=0.025,
    ...          order=3  )
    >>> model.train()
    >>> emb = model(g, return_dict=True) # g: easygraph.Graph or easygraph.DiGraph

    References
    ----------

    .. [1] Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015, May). Line: Large-scale information network embedding. In Proceedings of the 24th international conference on world wide web (pp. 1067-1077).

    https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp0228-Tang.pdf

    """

    @staticmethod
    def add_args(parser):
        """Add model-specific arguments to the parser."""
        parser.add_argument(
            "--walk-length",
            type=int,
            default=80,
            help="Length of walk per source. Default is 80.",
        )
        parser.add_argument(
            "--walk-num",
            type=int,
            default=20,
            help="Number of walks per source. Default is 20.",
        )
        parser.add_argument(
            "--negative",