def from_biadjacency_matrix(A, create_using=None, edge_attribute='weight'):
r"""Creates a new bipartite graph from a biadjacency matrix given as a
SciPy sparse matrix.
Parameters
----------
A: scipy sparse matrix
A biadjacency matrix representation of a graph
create_using: NetworkX graph
Use specified graph for result. The default is Graph()
edge_attribute: string
Name of edge attribute to store matrix numeric value. The data will
have the same type as the matrix entry (int, float, (real,imag)).
Notes
-----
The nodes are labeled with the attribute `bipartite` set to an integer
0 or 1 representing membership in part 0 or part 1 of the bipartite graph.
If `create_using` is an instance of :class:`networkx.MultiGraph` or
:class:`networkx.MultiDiGraph` and the entries of `A` are of
type :class:`int`, then this function returns a multigraph (of the same
type as `create_using`) with parallel edges. In this case, `edge_attribute`
will be ignored.
See Also
--------
biadjacency_matrix
from_numpy_matrix
References
----------
[1] https://en.wikipedia.org/wiki/Adjacency_matrix#Adjacency_matrix_of_a_bipartite_graph
"""
G = nx.empty_graph(0, create_using)
n, m = A.shape
# Make sure we get even the isolated nodes of the graph.
G.add_nodes_from(range(n), bipartite=0)
G.add_nodes_from(range(n, n + m), bipartite=1)
# Create an iterable over (u, v, w) triples and for each triple, add an
# edge from u to v with weight w.
triples = ((u, n + v, d) for (u, v, d) in _generate_weighted_edges(A))
# If the entries in the adjacency matrix are integers and the graph is a
# multigraph, then create parallel edges, each with weight 1, for each
# entry in the adjacency matrix. Otherwise, create one edge for each
# positive entry in the adjacency matrix and set the weight of that edge to
# be the entry in the matrix.
if A.dtype.kind in ('i', 'u') and G.is_multigraph():
chain = itertools.chain.from_iterable
triples = chain(((u, v, 1) for d in range(w)) for (u, v, w) in triples)
G.add_weighted_edges_from(triples, weight=edge_attribute)
return G
def from_biadjacency_matrix(A, create_using=None, edge_attribute='weight'):
r"""Creates a new bipartite graph from a biadjacency matrix given as a
SciPy sparse matrix.
Parameters
----------
A: scipy sparse matrix
A biadjacency matrix representation of a graph
create_using: NetworkX graph
Use specified graph for result. The default is Graph()
edge_attribute: string
Name of edge attribute to store matrix numeric value. The data will
have the same type as the matrix entry (int, float, (real,imag)).
Notes
-----
The nodes are labeled with the attribute `bipartite` set to an integer
0 or 1 representing membership in part 0 or part 1 of the bipartite graph.
If `create_using` is an instance of :class:`networkx.MultiGraph` or
:class:`networkx.MultiDiGraph` and the entries of `A` are of
type :class:`int`, then this function returns a multigraph (of the same
type as `create_using`) with parallel edges. In this case, `edge_attribute`
will be ignored.
See Also
--------
biadjacency_matrix
from_numpy_matrix
References
----------
[1] http://en.wikipedia.org/wiki/Adjacency_matrix#Adjacency_matrix_of_a_bipartite_graph
"""
G = _prep_create_using(create_using)
n, m = A.shape
# Make sure we get even the isolated nodes of the graph.
G.add_nodes_from(range(n), bipartite=0)
G.add_nodes_from(range(n,n+m), bipartite=1)
# Create an iterable over (u, v, w) triples and for each triple, add an
# edge from u to v with weight w.
triples = ((u, n+v, d) for (u, v, d) in _generate_weighted_edges(A))
# If the entries in the adjacency matrix are integers and the graph is a
# multigraph, then create parallel edges, each with weight 1, for each
# entry in the adjacency matrix. Otherwise, create one edge for each
# positive entry in the adjacency matrix and set the weight of that edge to
# be the entry in the matrix.
if A.dtype.kind in ('i', 'u') and G.is_multigraph():
chain = itertools.chain.from_iterable
triples = chain(((u, v, 1) for d in range(w)) for (u, v, w) in triples)
G.add_weighted_edges_from(triples, weight=edge_attribute)
return G
