biadjacency_matrix(G, row_order, column_order=None, dtype=None, weight='weight', format='csr')
Let :None:None:`G = (U, V, E)` be a bipartite graph with node sets :None:None:`U = u_{1},...,u_{r}` and :None:None:`V = v_{1},...,v_{s}`. The biadjacency matrix is the r x :None:None:`s` matrix :None:None:`B` in which :None:None:`b_{i,j} = 1` if, and only if, :None:None:`(u_i, v_j) \in E`. If the parameter :None:None:`weight` is not :None:None:`None` and matches the name of an edge attribute, its value is used instead of 1.
No attempt is made to check that the input graph is bipartite.
For directed bipartite graphs only successors are considered as neighbors. To obtain an adjacency matrix with ones (or weight values) for both predecessors and successors you have to generate two biadjacency matrices where the rows of one of them are the columns of the other, and then add one to the transpose of the other.
A NetworkX graph
The rows of the matrix are ordered according to the list of nodes.
The columns of the matrix are ordered according to the list of nodes. If column_order is None, then the ordering of columns is arbitrary.
A valid NumPy dtype used to initialize the array. If None, then the NumPy default is used.
The edge data key used to provide each value in the matrix. If None, then each edge has weight 1.
The type of the matrix to be returned (default 'csr'). For some algorithms different implementations of sparse matrices can perform better. See for details.
Biadjacency matrix representation of the bipartite graph G.
Returns the biadjacency matrix of the bipartite graph G.
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.bipartite.matrix.from_biadjacency_matrix
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