def test_lind_square_clustering(self):
"""Test C4 for figure 1 Lind et al (2005)"""
G = nx.Graph(
[
(1, 2),
(1, 3),
(1, 6),
(1, 7),
(2, 4),
(2, 5),
(3, 4),
(3, 5),
(6, 7),
(7, 8),
(6, 8),
(7, 9),
(7, 10),
(6, 11),
(6, 12),
(2, 13),
(2, 14),
(3, 15),
(3, 16),
]
)
G1 = G.subgraph([1, 2, 3, 4, 5, 13, 14, 15, 16])
G2 = G.subgraph([1, 6, 7, 8, 9, 10, 11, 12])
assert nx.square_clustering(G, [1])[1] == 3 / 75.0
assert nx.square_clustering(G1, [1])[1] == 2 / 6.0
assert nx.square_clustering(G2, [1])[1] == 1 / 5.0
def test_path(self):
G = nx.path_graph(10)
assert list(nx.square_clustering(G).values()) == [
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
assert nx.square_clustering(G) == {
0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
8: 0.0,
9: 0.0,
}
def test_cubical(self):
G = nx.cubical_graph()
assert list(nx.square_clustering(G).values()) == [
0.5,
0.5,
0.5,
0.5,
0.5,
0.5,
0.5,
0.5,
]
assert list(nx.square_clustering(G, [1, 2]).values()) == [0.5, 0.5]
assert nx.square_clustering(G, [1])[1] == 0.5
assert nx.square_clustering(G, [1, 2]) == {1: 0.5, 2: 0.5}
def test_bipartite_k5(self):
G = nx.complete_bipartite_graph(5, 5)
assert list(nx.square_clustering(G).values()) == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
def test_clustering(self):
G = nx.Graph()
assert list(nx.square_clustering(G).values()) == []
assert nx.square_clustering(G) == {}