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import pytest
import networkx as nx
from networkx.algorithms import bipartite
from networkx.algorithms.bipartite.cluster import cc_dot, cc_max, cc_min
def test_pairwise_bipartite_cc_functions():
# Test functions for different kinds of bipartite clustering coefficients
# between pairs of nodes using 3 example graphs from figure 5 p. 40
# Latapy et al (2008)
G1 = nx.Graph([(0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (1, 5), (1, 6), (1, 7)])
G2 = nx.Graph([(0, 2), (0, 3), (0, 4), (1, 3), (1, 4), (1, 5)])
G3 = nx.Graph(
[(0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9)]
)
result = {
0: [1 / 3.0, 2 / 3.0, 2 / 5.0],
1: [1 / 2.0, 2 / 3.0, 2 / 3.0],
2: [2 / 8.0, 2 / 5.0, 2 / 5.0],
}
for i, G in enumerate([G1, G2, G3]):
assert bipartite.is_bipartite(G)
assert cc_dot(set(G[0]), set(G[1])) == result[i][0]
assert cc_min(set(G[0]), set(G[1])) == result[i][1]
assert cc_max(set(G[0]), set(G[1])) == result[i][2]
def test_star_graph():
G = nx.star_graph(3)
# all modes are the same
answer = {0: 0, 1: 1, 2: 1, 3: 1}
assert bipartite.clustering(G, mode="dot") == answer
assert bipartite.clustering(G, mode="min") == answer
assert bipartite.clustering(G, mode="max") == answer
def test_not_bipartite():
with pytest.raises(nx.NetworkXError):
bipartite.clustering(nx.complete_graph(4))
def test_bad_mode():
with pytest.raises(nx.NetworkXError):
bipartite.clustering(nx.path_graph(4), mode="foo")
def test_path_graph():
G = nx.path_graph(4)
answer = {0: 0.5, 1: 0.5, 2: 0.5, 3: 0.5}
assert bipartite.clustering(G, mode="dot") == answer
assert bipartite.clustering(G, mode="max") == answer
answer = {0: 1, 1: 1, 2: 1, 3: 1}
assert bipartite.clustering(G, mode="min") == answer
def test_average_path_graph():
G = nx.path_graph(4)
assert bipartite.average_clustering(G, mode="dot") == 0.5
assert bipartite.average_clustering(G, mode="max") == 0.5
assert bipartite.average_clustering(G, mode="min") == 1
def test_ra_clustering_davis():
G = nx.davis_southern_women_graph()
cc4 = round(bipartite.robins_alexander_clustering(G), 3)
assert cc4 == 0.468
def test_ra_clustering_square():
G = nx.path_graph(4)
G.add_edge(0, 3)
assert bipartite.robins_alexander_clustering(G) == 1.0
def test_ra_clustering_zero():
G = nx.Graph()
assert bipartite.robins_alexander_clustering(G) == 0
G.add_nodes_from(range(4))
assert bipartite.robins_alexander_clustering(G) == 0
G.add_edges_from([(0, 1), (2, 3), (3, 4)])
assert bipartite.robins_alexander_clustering(G) == 0
G.add_edge(1, 2)
assert bipartite.robins_alexander_clustering(G) == 0
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