torch_geometric_signed_directed.data.signed.SSBM
================================================

.. py:module:: torch_geometric_signed_directed.data.signed.SSBM


Functions
---------

.. autoapisummary::

   torch_geometric_signed_directed.data.signed.SSBM.SSBM
   torch_geometric_signed_directed.data.signed.SSBM.fill


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

.. py:function:: SSBM(n: int, k: int, pin: float, etain: float, pout: Optional[float] = None, size_ratio: float = 2, etaout: Optional[float] = None, values: str = 'ones') -> Tuple[Tuple[scipy.sparse.spmatrix, scipy.sparse.spmatrix], numpy.array]

   A signed stochastic block model graph generator from the
   `SSSNET: Semi-Supervised Signed Network Clustering <https://arxiv.org/pdf/2110.06623.pdf>`_ paper.

   Arg types:
       * **n** (int) - Number of nodes.
       * **k** (int) - Number of communities.
       * **pin** (float) - Sparsity value within communities.
       * **etain** (float) - Noise value within communities.
       * **pout** (float) - Sparsity value between communities.
       * **etaout** (float) - Noise value between communities.
       * **size_ratio** (float) - The communities have number of nodes multiples of each other, with the largest size_ratio times the number of nodes of the smallest.
       * **values** (string) - Edge weight distribution (within community and without sign flip; otherwise weight is negated):

           1. :obj:`ones`: Weights are 1.

           2. :obj:`"exp"`: Weights are exponentially distributed, with parameter 1.

           3. :obj:`"uniform"`: Weights are uniformly distributed between 0 and 1.

   Return types:
       * **A_p** (sp.spmatrix) - A sparse adjacency matrix for the positive part.
       * **A_n** (sp.spmatrix) - A sparse adjacency matrix for the negative part.
       * **labels** (np.array) - Labels.



.. py:function:: fill(values: str = 'ones') -> float

   A filling method for the signed stochastic block model graph generator from the
   `SSSNET: Semi-Supervised Signed Network Clustering" <https://arxiv.org/pdf/2110.06623.pdf>`_ paper.
   Arg types:
       * **values** (string): Edge weight:

           'ones': Weights are 1.

           'exp': Weights are exponentially distributed, with parameter 1.

           'uniform: Weights are uniformly distributed between 0 and 1.
   Return types:
       * **value** (float): A filled value.