def test_graph_disconnect():
"""Simple disconnected undirected graph, used for testing.
10 nodes, 10 edges.
"""
row = np.array([1, 2, 3, 4, 6, 6, 6, 7, 8, 9])
col = np.array([1, 3, 2, 5, 4, 5, 7, 9, 9, 9])
data = np.array([1, 2.5, 1, 2, 2, 1, 2, 2, 1.5, 2])
adjacency = sparse.csr_matrix((data, (row, col)), shape=(10, 10))
return directed2undirected(adjacency)
def edgelist2adjacency(edgelist: list,
undirected: bool = False) -> sparse.csr_matrix:
"""Build an adjacency matrix from a list of edges.
Parameters
----------
edgelist : list
List of edges as pairs (i, j) or triplets (i, j, w) for weighted edges.
undirected : bool
If ``True``, return a symmetric adjacency.
Returns
-------
adjacency : sparse.csr_matrix
Examples
--------
>>> edgelist = [(0, 1), (1, 2), (2, 0)]
>>> adjacency = edgelist2adjacency(edgelist)
>>> adjacency.shape, adjacency.nnz
((3, 3), 3)
>>> adjacency = edgelist2adjacency(edgelist, undirected=True)
>>> adjacency.shape, adjacency.nnz
((3, 3), 6)
>>> weighted_edgelist = [(0, 1, 0.2), (1, 2, 4), (2, 0, 1.3)]
>>> adjacency = edgelist2adjacency(weighted_edgelist)
>>> adjacency.dtype
dtype('float64')
"""
edges = np.array(edgelist)
row, col = edges[:, 0].astype(np.int32), edges[:, 1].astype(np.int32)
n = max(row.max(), col.max()) + 1
if edges.shape[1] > 2:
data = edges[:, 2]
else:
data = np.ones_like(row, dtype=bool)
adjacency = sparse.csr_matrix((data, (row, col)), shape=(n, n))
if undirected:
adjacency = directed2undirected(adjacency)
return adjacency
def test_graph():
"""Simple undirected graph, used for testing.
10 nodes, 10 edges.
"""
return directed2undirected(test_digraph(), weighted=True)