torch_geometric_signed_directed.nn.signed.SiGAT
===============================================

.. py:module:: torch_geometric_signed_directed.nn.signed.SiGAT


Classes
-------

.. autoapisummary::

   torch_geometric_signed_directed.nn.signed.SiGAT.SiGAT


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

.. py:class:: SiGAT(node_num: int, edge_index_s, in_dim: int = 20, out_dim: int = 20, init_emb: torch.FloatTensor = None, init_emb_grad: bool = True, **kwargs)

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


   The signed graph attention network model (SiGAT) from the `"Signed Graph
   Attention Networks" <https://arxiv.org/abs/1906.10958>`_ paper.

   :Parameters: * **node_num** (*[type]*) -- Number of node.
                * **edge_index_s** (*list*) -- The edgelist with sign. (e.g., [[0, 1, -1]] )
                * **in_dim** (*int, optional*) -- Size of each input sample features. Defaults to 20.
                * **out_dim** (*int*) -- Size of each output embeddings. Defaults to 20.
                * **init_emb** -- (FloatTensor, optional): The initial embeddings. Defaults to :obj:`None`, which will use TSVD as initial embeddings.
                * **init_emb_grad** (*bool optional*) -- Whether to set the initial embeddings to be trainable. (default: :obj:`False`)


   .. py:attribute:: in_dim
      :value: 20



   .. py:attribute:: out_dim
      :value: 20



   .. py:attribute:: node_num


   .. py:attribute:: device


   .. py:attribute:: pos_edge_index


   .. py:attribute:: neg_edge_index


   .. py:attribute:: x


   .. py:attribute:: adj_lists


   .. py:attribute:: edge_lists


   .. py:attribute:: aggs
      :value: []



   .. py:attribute:: mlp_layer


   .. py:attribute:: lsp_loss


   .. py:method:: reset_parameters()


   .. py:method:: map_adj_to_edges(adj_list: List) -> torch.LongTensor


   .. py:method:: get_tri_features(u: int, v: int, r_edgelist: List) -> Tuple[int, int, int, int, int, int, int, int, int, int, int, int, int, int, int, int]


   .. py:method:: build_adj_lists(edge_index_s: torch.LongTensor) -> List


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


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