torch_geometric_signed_directed.nn.signed.SNEA
==============================================

.. py:module:: torch_geometric_signed_directed.nn.signed.SNEA


Classes
-------

.. autoapisummary::

   torch_geometric_signed_directed.nn.signed.SNEA.SNEA


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

.. py:class:: SNEA(node_num: int, edge_index_s: torch.LongTensor, in_dim: int = 64, out_dim: int = 64, layer_num: int = 2, init_emb: torch.FloatTensor = None, init_emb_grad: bool = True, lamb: float = 4)

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


   The signed graph attentional layers operator from the `"Learning Signed
   Network Embedding via Graph Attention" <https://ojs.aaai.org/index.php/AAAI/article/view/5911>`_ paper
   :Parameters: * **node_num** (*int*) -- The number of nodes.
                * **edge_index_s** (*LongTensor*) -- The edgelist with sign. (e.g., torch.LongTensor([[0, 1, -1], [0, 2, 1]]) )
                * **in_dim** (*int, optional*) -- Size of each input sample features. Defaults to 64.
                * **out_dim** (*int, optional*) -- Size of each output embeddings. Defaults to 64.
                * **layer_num** (*int, optional*) -- Number of layers. Defaults to 2.
                * **init_emb** -- (FloatTensor, optional): The initial embeddings. Defaults to :obj:`None`, which will use TSVD as initial embeddings.
                * **init_emb_grad** (*bool, optional*) -- Optimize initial embeddings or not.
                * **lamb** (*float, optional*) -- Balances the contributions of the overall
                  objective. (default: :obj:`4`)


   .. py:attribute:: node_num


   .. py:attribute:: in_dim
      :value: 64



   .. py:attribute:: out_dim
      :value: 64



   .. py:attribute:: lamb
      :value: 4



   .. py:attribute:: device


   .. py:attribute:: pos_edge_index


   .. py:attribute:: neg_edge_index


   .. py:attribute:: x


   .. py:attribute:: conv1


   .. py:attribute:: convs


   .. py:attribute:: weight


   .. py:attribute:: lsp_loss


   .. py:attribute:: structure_loss


   .. py:method:: reset_parameters()


   .. py:method:: loss() -> torch.FloatTensor


   .. py:method:: forward() -> torch.Tensor