Method v2e_of_group

easygraph/classes/hypergraph.py:1483–1509

r"""Message passing of ``vertices to hyperedges`` in specified hyperedge group. The combination of ``e2v_aggregation_of_group`` and ``e2v_update_of_group``. Args: ``group_name`` (``str``): The specified hyperedge group. ``X`` (``torch.Tensor``): Vertex feature matrix

(
        self,
        group_name: str,
        X: torch.Tensor,
        aggr: str = "mean",
        v2e_weight: Optional[torch.Tensor] = None,
        e_weight: Optional[torch.Tensor] = None,
        drop_rate: float = 0.0,
    )

        return X

    def v2e_of_group(
        self,
        group_name: str,
        X: torch.Tensor,
        aggr: str = "mean",
        v2e_weight: Optional[torch.Tensor] = None,
        e_weight: Optional[torch.Tensor] = None,
        drop_rate: float = 0.0,
    ):
        r"""Message passing of ``vertices to hyperedges`` in specified hyperedge group. The combination of ``e2v_aggregation_of_group`` and ``e2v_update_of_group``.

        Args:
            ``group_name`` (``str``): The specified hyperedge group.
            ``X`` (``torch.Tensor``): Vertex feature matrix. Size :math:`(|\mathcal{V}|, C)`.
            ``aggr`` (``str``): The aggregation method. Can be ``'mean'``, ``'sum'`` and ``'softmax_then_sum'``.
            ``v2e_weight`` (``torch.Tensor``, optional): The weight vector attached to connections (vertices point to hyepredges). If not specified, the function will use the weights specified in hypergraph construction. Defaults to ``None``.
            ``e_weight`` (``torch.Tensor``, optional): The hyperedge weight vector. If not specified, the function will use the weights specified in hypergraph construction. Defaults to ``None``.
            ``drop_rate`` (``float``): Dropout rate. Randomly dropout the connections in incidence matrix with probability ``drop_rate``. Default: ``0.0``.
        """
        assert (
            group_name in self.group_names
        ), f"The specified {group_name} is not in existing hyperedge groups."
        X = self.v2e_aggregation_of_group(
            group_name, X, aggr, v2e_weight, drop_rate=drop_rate
        )
        X = self.v2e_update_of_group(group_name, X, e_weight)
        return X

    def e2v_aggregation(
        self,