import pytest
import networkx as nx
from networkx.algorithms import bipartite
class TestBipartiteCentrality:
@classmethod
def setup_class(cls):
cls.P4 = nx.path_graph(4)
cls.K3 = nx.complete_bipartite_graph(3, 3)
cls.C4 = nx.cycle_graph(4)
cls.davis = nx.davis_southern_women_graph()
cls.top_nodes = [
n for n, d in cls.davis.nodes(data=True) if d["bipartite"] == 0
]
def test_degree_centrality(self):
d = bipartite.degree_centrality(self.P4, [1, 3])
answer = {0: 0.5, 1: 1.0, 2: 1.0, 3: 0.5}
assert d == answer
d = bipartite.degree_centrality(self.K3, [0, 1, 2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0}
assert d == answer
d = bipartite.degree_centrality(self.C4, [0, 2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}
assert d == answer
def test_betweenness_centrality(self):
c = bipartite.betweenness_centrality(self.P4, [1, 3])
answer = {0: 0.0, 1: 1.0, 2: 1.0, 3: 0.0}
assert c == answer
c = bipartite.betweenness_centrality(self.K3, [0, 1, 2])
answer = {0: 0.125, 1: 0.125, 2: 0.125, 3: 0.125, 4: 0.125, 5: 0.125}
assert c == answer
c = bipartite.betweenness_centrality(self.C4, [0, 2])
answer = {0: 0.25, 1: 0.25, 2: 0.25, 3: 0.25}
assert c == answer
def test_closeness_centrality(self):
c = bipartite.closeness_centrality(self.P4, [1, 3])
answer = {0: 2.0 / 3, 1: 1.0, 2: 1.0, 3: 2.0 / 3}
assert c == answer
c = bipartite.closeness_centrality(self.K3, [0, 1, 2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0}
assert c == answer
c = bipartite.closeness_centrality(self.C4, [0, 2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}
assert c == answer
G = nx.Graph()
G.add_node(0)
G.add_node(1)
c = bipartite.closeness_centrality(G, [0])
assert c == {0: 0.0, 1: 0.0}
c = bipartite.closeness_centrality(G, [1])
assert c == {0: 0.0, 1: 0.0}
def test_bipartite_closeness_centrality_unconnected(self):
G = nx.complete_bipartite_graph(3, 3)
G.add_edge(6, 7)
c = bipartite.closeness_centrality(G, [0, 2, 4, 6], normalized=False)
answer = {
0: 10.0 / 7,
2: 10.0 / 7,
4: 10.0 / 7,
6: 10.0,
1: 10.0 / 7,
3: 10.0 / 7,
5: 10.0 / 7,
7: 10.0,
}
assert c == answer
def test_davis_degree_centrality(self):
G = self.davis
deg = bipartite.degree_centrality(G, self.top_nodes)
answer = {
"E8": 0.78,
"E9": 0.67,
"E7": 0.56,
"Nora Fayette": 0.57,
"Evelyn Jefferson": 0.57,
"Theresa Anderson": 0.57,
"E6": 0.44,
"Sylvia Avondale": 0.50,
"Laura Mandeville": 0.50,
"Brenda Rogers": 0.50,
"Katherina Rogers": 0.43,
"E5": 0.44,
"Helen Lloyd": 0.36,
"E3": 0.33,
"Ruth DeSand": 0.29,
"Verne Sanderson": 0.29,
"E12": 0.33,
"Myra Liddel": 0.29,
"E11": 0.22,
"Eleanor Nye": 0.29,
"Frances Anderson": 0.29,
"Pearl Oglethorpe": 0.21,
"E4": 0.22,
"Charlotte McDowd": 0.29,
"E10": 0.28,
"Olivia Carleton": 0.14,
"Flora Price": 0.14,
"E2": 0.17,
"E1": 0.17,
"Dorothy Murchison": 0.14,
"E13": 0.17,
"E14": 0.17,
}
for node, value in answer.items():
assert value == pytest.approx(deg[node], abs=1e-2)
def test_davis_betweenness_centrality(self):
G = self.davis
bet = bipartite.betweenness_centrality(G, self.top_nodes)
answer = {
"E8": 0.24,
"E9": 0.23,
"E7": 0.13,
"Nora Fayette": 0.11,
"Evelyn Jefferson": 0.10,
"Theresa Anderson": 0.09,
"E6": 0.07,
"Sylvia Avondale": 0.07,
"Laura Mandeville": 0.05,
"Brenda Rogers": 0.05,
"Katherina Rogers": 0.05,
"E5": 0.04,
"Helen Lloyd": 0.04,
"E3": 0.02,
"Ruth DeSand": 0.02,
"Verne Sanderson": 0.02,
"E12": 0.02,
"Myra Liddel": 0.02,
"E11": 0.02,
"Eleanor Nye": 0.01,
"Frances Anderson": 0.01,
"Pearl Oglethorpe": 0.01,
"E4": 0.01,
"Charlotte McDowd": 0.01,
"E10": 0.01,
"Olivia Carleton": 0.01,
"Flora Price": 0.01,
"E2": 0.00,
"E1": 0.00,
"Dorothy Murchison": 0.00,
"E13": 0.00,
"E14": 0.00,
}
for node, value in answer.items():
assert value == pytest.approx(bet[node], abs=1e-2)
def test_davis_closeness_centrality(self):
G = self.davis
clos = bipartite.closeness_centrality(G, self.top_nodes)
answer = {
"E8": 0.85,
"E9": 0.79,
"E7": 0.73,
"Nora Fayette": 0.80,
"Evelyn Jefferson": 0.80,
"Theresa Anderson": 0.80,
"E6": 0.69,
"Sylvia Avondale": 0.77,
"Laura Mandeville": 0.73,
"Brenda Rogers": 0.73,
"Katherina Rogers": 0.73,
"E5": 0.59,
"Helen Lloyd": 0.73,
"E3": 0.56,
"Ruth DeSand": 0.71,
"Verne Sanderson": 0.71,
"E12": 0.56,
"Myra Liddel": 0.69,
"E11": 0.54,
"Eleanor Nye": 0.67,
"Frances Anderson": 0.67,
"Pearl Oglethorpe": 0.67,
"E4": 0.54,
"Charlotte McDowd": 0.60,
"E10": 0.55,
"Olivia Carleton": 0.59,
"Flora Price": 0.59,
"E2": 0.52,
"E1": 0.52,
"Dorothy Murchison": 0.65,
"E13": 0.52,
"E14": 0.52,
}
for node, value in answer.items():
assert value == pytest.approx(clos[node], abs=1e-2)
