175 lines
6.2 KiB
Python
175 lines
6.2 KiB
Python
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"""
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====================
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Biadjacency matrices
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====================
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"""
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import itertools
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import networkx as nx
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from networkx.convert_matrix import _generate_weighted_edges
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__all__ = ["biadjacency_matrix", "from_biadjacency_matrix"]
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def biadjacency_matrix(
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G, row_order, column_order=None, dtype=None, weight="weight", format="csr"
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):
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r"""Returns the biadjacency matrix of the bipartite graph G.
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Let `G = (U, V, E)` be a bipartite graph with node sets
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`U = u_{1},...,u_{r}` and `V = v_{1},...,v_{s}`. The biadjacency
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matrix [1]_ is the `r` x `s` matrix `B` in which `b_{i,j} = 1`
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if, and only if, `(u_i, v_j) \in E`. If the parameter `weight` is
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not `None` and matches the name of an edge attribute, its value is
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used instead of 1.
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Parameters
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----------
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G : graph
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A NetworkX graph
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row_order : list of nodes
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The rows of the matrix are ordered according to the list of nodes.
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column_order : list, optional
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The columns of the matrix are ordered according to the list of nodes.
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If column_order is None, then the ordering of columns is arbitrary.
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dtype : NumPy data-type, optional
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A valid NumPy dtype used to initialize the array. If None, then the
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NumPy default is used.
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weight : string or None, optional (default='weight')
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The edge data key used to provide each value in the matrix.
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If None, then each edge has weight 1.
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format : str in {'bsr', 'csr', 'csc', 'coo', 'lil', 'dia', 'dok'}
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The type of the matrix to be returned (default 'csr'). For
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some algorithms different implementations of sparse matrices
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can perform better. See [2]_ for details.
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Returns
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-------
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M : SciPy sparse matrix
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Biadjacency matrix representation of the bipartite graph G.
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Notes
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-----
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No attempt is made to check that the input graph is bipartite.
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For directed bipartite graphs only successors are considered as neighbors.
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To obtain an adjacency matrix with ones (or weight values) for both
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predecessors and successors you have to generate two biadjacency matrices
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where the rows of one of them are the columns of the other, and then add
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one to the transpose of the other.
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See Also
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--------
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adjacency_matrix
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from_biadjacency_matrix
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References
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----------
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.. [1] https://en.wikipedia.org/wiki/Adjacency_matrix#Adjacency_matrix_of_a_bipartite_graph
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.. [2] Scipy Dev. References, "Sparse Matrices",
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https://docs.scipy.org/doc/scipy/reference/sparse.html
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"""
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import scipy as sp
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import scipy.sparse # call as sp.sparse
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nlen = len(row_order)
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if nlen == 0:
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raise nx.NetworkXError("row_order is empty list")
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if len(row_order) != len(set(row_order)):
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msg = "Ambiguous ordering: `row_order` contained duplicates."
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raise nx.NetworkXError(msg)
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if column_order is None:
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column_order = list(set(G) - set(row_order))
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mlen = len(column_order)
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if len(column_order) != len(set(column_order)):
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msg = "Ambiguous ordering: `column_order` contained duplicates."
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raise nx.NetworkXError(msg)
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row_index = dict(zip(row_order, itertools.count()))
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col_index = dict(zip(column_order, itertools.count()))
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if G.number_of_edges() == 0:
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row, col, data = [], [], []
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else:
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row, col, data = zip(
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*(
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(row_index[u], col_index[v], d.get(weight, 1))
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for u, v, d in G.edges(row_order, data=True)
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if u in row_index and v in col_index
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)
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)
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# TODO: change coo_matrix -> coo_array for NX 3.0
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A = sp.sparse.coo_matrix((data, (row, col)), shape=(nlen, mlen), dtype=dtype)
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try:
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import warnings
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warnings.warn(
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"biadjacency_matrix will return a scipy.sparse array instead of a matrix in NetworkX 3.0",
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FutureWarning,
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stacklevel=2,
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)
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return A.asformat(format)
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except ValueError as err:
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raise nx.NetworkXError(f"Unknown sparse array format: {format}") from err
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def from_biadjacency_matrix(A, create_using=None, edge_attribute="weight"):
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r"""Creates a new bipartite graph from a biadjacency matrix given as a
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SciPy sparse matrix.
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Parameters
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----------
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A: scipy sparse matrix
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A biadjacency matrix representation of a graph
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create_using: NetworkX graph
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Use specified graph for result. The default is Graph()
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edge_attribute: string
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Name of edge attribute to store matrix numeric value. The data will
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have the same type as the matrix entry (int, float, (real,imag)).
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Notes
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-----
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The nodes are labeled with the attribute `bipartite` set to an integer
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0 or 1 representing membership in part 0 or part 1 of the bipartite graph.
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If `create_using` is an instance of :class:`networkx.MultiGraph` or
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:class:`networkx.MultiDiGraph` and the entries of `A` are of
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type :class:`int`, then this function returns a multigraph (of the same
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type as `create_using`) with parallel edges. In this case, `edge_attribute`
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will be ignored.
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See Also
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--------
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biadjacency_matrix
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from_numpy_array
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References
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----------
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[1] https://en.wikipedia.org/wiki/Adjacency_matrix#Adjacency_matrix_of_a_bipartite_graph
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"""
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G = nx.empty_graph(0, create_using)
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n, m = A.shape
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# Make sure we get even the isolated nodes of the graph.
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G.add_nodes_from(range(n), bipartite=0)
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G.add_nodes_from(range(n, n + m), bipartite=1)
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# Create an iterable over (u, v, w) triples and for each triple, add an
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# edge from u to v with weight w.
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triples = ((u, n + v, d) for (u, v, d) in _generate_weighted_edges(A))
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# If the entries in the adjacency matrix are integers and the graph is a
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# multigraph, then create parallel edges, each with weight 1, for each
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# entry in the adjacency matrix. Otherwise, create one edge for each
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# positive entry in the adjacency matrix and set the weight of that edge to
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# be the entry in the matrix.
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if A.dtype.kind in ("i", "u") and G.is_multigraph():
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chain = itertools.chain.from_iterable
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triples = chain(((u, v, 1) for d in range(w)) for (u, v, w) in triples)
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G.add_weighted_edges_from(triples, weight=edge_attribute)
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return G
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