Network sampling

Functions for random sampling of weighted directed networks are in motifcluster.sampling.

demonstration_graph()

Generate a small graph for demonstrations.

Generate the sparse and dense adjacency matrices of a small weighted directed graph, for demonstrating methods and running tests.

Returns:
  • adj_mat_dense (matrix) – the adjacency matrix in dense form.
  • adj_mat_sparse (sparse matrix) – the adjacency matrix in sparse form.
sample_bsbm(source_block_sizes, dest_block_sizes, bipartite_connection_matrix, bipartite_weight_matrix=None, sample_weight_type='unweighted')

Sample a bipartite stochastic block model (BSBM).

Sample the (weighted) adjacency matrix of a (weighted) bipartite stochastic block model (BSBM) with specified parameters.

Parameters:
  • source_block_sizes (list of int) – A list containing the size of each block of source vertices.
  • dest_block_sizes (list of int) – A list containing the size of each block of destination vertices.
  • bipartite_connection_matrix (matrix) – A matrix containing the source block to destination block connection probabilities.
  • bipartite_weight_matrix (matrix) – A matrix containing the source block to destination block weight parameters. Unused for sample_weight_type = “constant”. Defaults to None.
  • sample_weight_type (str) – The type of weighting scheme. One of “unweighted”, “constant” or “poisson”.
Returns:

adj_mat – A randomly sampled (weighted) adjacency matrix of a BSBM.
Return type:

sparse matrix

Examples

>>> source_block_sizes = [10, 10]
>>> dest_block_sizes = [10, 10, 10]
>>> bipartite_connection_matrix = np.array([0.8, 0.5, 0.1, 0.1, 0.5, 0.8]).reshape((2, 3))
>>> bipartite_weight_matrix = np.array([20, 10, 2, 2, 10, 20]).reshape((2, 3))
>>> sample_bsbm(block_sizes, bipartite_connection_matrix,
...   bipartite_weight_matrix, "poisson")
sample_dsbm(block_sizes, connection_matrix, weight_matrix=None, sample_weight_type='unweighted')

Sample a directed stochastic block model (DSBM).

Sample the (weighted) adjacency matrix of a (weighted) directed stochastic block model (DSBM) with specified parameters.

Parameters:
  • block_sizes (list of int) – A list containing the size of each block of vertices.
  • connection_matrix (matrix) – A matrix containing the block-to-block connection probabilities.
  • sample_weight_type (str) – The type of weighting scheme. One of “unweighted”, “constant” or “poisson”.
  • weight_matrix (matrix) – A matrix containing the block-to-block weight parameters. Unused for sample_weight_type = “constant”. Defaults to None.
Returns:

adj_mat – A randomly sampled (weighted) adjacency matrix of a DSBM.
Return type:

sparse matrix

Examples

>>> block_sizes = [10, 10]
>>> connection_matrix = np.array([0.8, 0.1, 0.1, 0.8]).reshape((2, 2))
>>> weight_matrix = np.array([10, 3, 3, 10]).reshape((2, 2))
>>> sample_dsbm(block_sizes, connection_matrix, weight_matrix, "poisson")