def test_betweenness_centrality(self): c = nx.bipartite_betweenness_centrality(self.P4, [1, 3]) answer = {0: 0.0, 1: 1.0, 2: 1.0, 3: 0.0} assert_equal(c, answer) c = nx.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_equal(c, answer) c = nx.bipartite_betweenness_centrality(self.C4, [0, 2]) answer = {0: 0.25, 1: 0.25, 2: 0.25, 3: 0.25} assert_equal(c, answer)
def test_betweenness_centrality(self): c = nx.bipartite_betweenness_centrality(self.P4, [1,3]) answer = {0: 0.0, 1: 1.0, 2: 1.0, 3: 0.0} assert_equal(c, answer) c = nx.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_equal(c, answer) c = nx.bipartite_betweenness_centrality(self.C4, [0,2]) answer = {0: 0.25, 1: 0.25, 2: 0.25, 3: 0.25} assert_equal(c, answer)
def test_davis_betweenness_centrality(self): G = self.davis bet = nx.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_almost_equal(value, bet[node], places=2)
def test_davis_betweenness_centrality(self): G = self.davis bet = nx.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_almost_equal(value, bet[node], places=2)