torch_geometric_signed_directed.data.directed.DSBM
==================================================

.. py:module:: torch_geometric_signed_directed.data.directed.DSBM


Functions
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.. autoapisummary::

   torch_geometric_signed_directed.data.directed.DSBM.DSBM


Module Contents
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.. py:function:: DSBM(N: int, K: int, p: float, F: numpy.array, size_ratio: float = 1) -> Tuple[scipy.sparse.spmatrix, numpy.array]

   A directed stochastic block model graph generator from the
   `DIGRAC: Digraph Clustering Based on Flow Imbalance <https://proceedings.mlr.press/v198/he22b.html>`_ paper.

   Arg types:
       * **N** (int) - Number of nodes.
       * **K** (int) - Number of clusters.
       * **p** (float) - Sparsity value, edge probability.
       * **F** (np.array) - The meta-graph adjacency matrix to generate edges.
       * **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.             A geometric sequence is generated to denote the node size of each cluster based on the size_ratio.

   Return types:
       * **a** (sp.csr_matrix) - a is a sparse N by N matrix of the edges.
       * **c** (np.array) - c is an array of cluster membership.