torch_geometric_signed_directed.nn.signed.SIMPA
===============================================

.. py:module:: torch_geometric_signed_directed.nn.signed.SIMPA


Classes
-------

.. autoapisummary::

   torch_geometric_signed_directed.nn.signed.SIMPA.SIMPA


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

.. py:class:: SIMPA(hop: int, fill_value: float, directed: bool = False)

   Bases: :py:obj:`torch.nn.Module`


   The signed mixed-path aggregation model from the
   `SSSNET: Semi-Supervised Signed Network Clustering <https://arxiv.org/pdf/2110.06623.pdf>`_ paper.

   :Parameters: * **hop** (*int*) -- Number of hops to consider.
                * **fill_value** (*float*) -- Value for added self-loops for the positive part of the adjacency matrix.
                * **directed** (*bool, optional*) -- Whether the input network is directed or not. (default: :obj:`False`)


   .. py:attribute:: conv_layer_p


   .. py:attribute:: conv_layer_n


   .. py:method:: forward(edge_index_p: torch.LongTensor, edge_weight_p: torch.FloatTensor, edge_index_n: torch.LongTensor, edge_weight_n: torch.FloatTensor, x_p: torch.FloatTensor, x_n: torch.FloatTensor, x_pt: Optional[torch.FloatTensor] = None, x_nt: Optional[torch.FloatTensor] = None) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.LongTensor, torch.FloatTensor]

      Making a forward pass of SIMPA.

      Arg types:
          * **edge_index_p, edge_index_n** (PyTorch FloatTensor) - Edge indices for positive and negative parts.
          * **edge_weight_p, edge_weight_n** (PyTorch FloatTensor) - Edge weights for positive and nagative parts.
          * **x_p** (PyTorch FloatTensor) - Souce positive hidden representations.
          * **x_n** (PyTorch FloatTensor) - Souce negative hidden representations.
          * **x_pt** (PyTorch FloatTensor, optional) - Target positive hidden representations. Default: None.
          * **x_nt** (PyTorch FloatTensor, optional) - Target negative hidden representations. Default: None.
      Return types:
          * **feat** (PyTorch FloatTensor) - Embedding matrix, with shape (num_nodes, 2*input_dim) for undirected graphs and (num_nodes, 4*input_dim) for directed graphs.