torch_geometric_signed_directed.nn.directed.DGCNConv
====================================================

.. py:module:: torch_geometric_signed_directed.nn.directed.DGCNConv


Classes
-------

.. autoapisummary::

   torch_geometric_signed_directed.nn.directed.DGCNConv.DGCNConv


Module Contents
---------------

.. py:class:: DGCNConv(improved: bool = False, cached: bool = False, add_self_loops: bool = True, normalize: bool = True, **kwargs)

   Bases: :py:obj:`torch_geometric.nn.conv.MessagePassing`


   An implementatino of the graph convolutional operator from the
   `Directed Graph Convolutional Network
   <https://arxiv.org/pdf/2004.13970.pdf>`_ paper.
   The same as Kipf's GCN but remove trainable weights.

   :Parameters: * **improved** (*bool, optional*) -- If set to :obj:`True`, the layer computes
                  :math:`\mathbf{\hat{A}}` as :math:`\mathbf{A} + 2\mathbf{I}`.
                  (default: :obj:`False`)
                * **cached** (*bool, optional*) -- If set to :obj:`True`, the layer will cache
                  the computation of :math:`\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}}
                  \mathbf{\hat{D}}^{-1/2}` on first execution, and will use the
                  cached version for further executions.
                  This parameter should only be set to :obj:`True` in transductive
                  learning scenarios. (default: :obj:`False`)
                * **add_self_loops** (*bool, optional*) -- If set to :obj:`False`, will not add
                  self-loops to the input graph. (default: :obj:`True`)
                * **normalize** (*bool, optional*) -- Whether to add self-loops and compute
                  symmetric normalization coefficients on the fly.
                  (default: :obj:`True`)
                * **\*\*kwargs** (*optional*) -- Additional arguments of
                  :class:`torch_geometric.nn.conv.MessagePassing`.


   .. py:attribute:: improved
      :value: False



   .. py:attribute:: cached
      :value: False



   .. py:attribute:: add_self_loops
      :value: True



   .. py:attribute:: normalize
      :value: True



   .. py:method:: reset_parameters()


   .. py:method:: forward(x: torch.Tensor, edge_index: torch_geometric.typing.Adj, edge_weight: torch_geometric.typing.OptTensor = None) -> torch.Tensor

      Making a forward pass of the graph convolutional operator.

      Arg types:
          * x (PyTorch FloatTensor) - Node features.
          * edge_index (Adj) - Edge indices.
          * edge_weight (OptTensor, optional) - Edge weights corresponding to edge indices.
      Return types:
          * out (PyTorch FloatTensor) - Hidden state tensor for all nodes.



   .. py:method:: message(x_j: torch.Tensor, edge_weight: torch_geometric.typing.OptTensor) -> torch.Tensor


   .. py:method:: message_and_aggregate(adj_t: torch_geometric.typing.SparseTensor, x: torch.Tensor) -> torch.Tensor