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