torch_geometric_signed_directed.nn.directed.DiGCNConv
=====================================================

.. py:module:: torch_geometric_signed_directed.nn.directed.DiGCNConv


Classes
-------

.. autoapisummary::

   torch_geometric_signed_directed.nn.directed.DiGCNConv.DiGCNConv


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

.. py:class:: DiGCNConv(in_channels: int, out_channels: int, improved: bool = False, cached: bool = True, bias: bool = True, **kwargs)

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


   The graph convolutional operator from the
   `Digraph Inception Convolutional Networks
   <https://papers.nips.cc/paper/2020/file/cffb6e2288a630c2a787a64ccc67097c-Paper.pdf>`_ paper.
   The spectral operation is the same with Kipf's GCN.
   DiGCN preprocesses the adjacency matrix and does not require a norm operation during the convolution operation.

   :Parameters: * **in_channels** (*int*) -- Size of each input sample.
                * **out_channels** (*int*) -- Size of each output sample.
                * **cached** (*bool, optional*) -- If set to :obj:`True`, the layer will cache
                  the adj matrix on first execution, and will use the
                  cached version for further executions.
                  Please note that, all the normalized adj matrices (including undirected)
                  are calculated in the dataset preprocessing to reduce time comsume.
                  This parameter should only be set to :obj:`True` in transductive
                  learning scenarios. (default: :obj:`False`)
                * **bias** (*bool, optional*) -- If set to :obj:`False`, the layer will not learn
                  an additive bias. (default: :obj:`True`)
                * **\*\*kwargs** (*optional*) -- Additional arguments of
                  :class:`torch_geometric.nn.conv.MessagePassing`.


   .. py:attribute:: in_channels


   .. py:attribute:: out_channels


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



   .. py:attribute:: cached
      :value: True



   .. py:attribute:: weight


   .. py:method:: reset_parameters()


   .. py:method:: forward(x: torch.FloatTensor, edge_index: torch.LongTensor, edge_weight: torch.FloatTensor = None) -> torch.FloatTensor

      Making a forward pass of the DiGCN Convolution layer.

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



   .. py:method:: message(x_j, norm)


   .. py:method:: update(aggr_out)