### view env/lib/python3.9/site-packages/networkx/algorithms/centrality/tests/test_betweenness_centrality.py @ 0:4f3585e2f14bdraftdefaulttip

author shellac Mon, 22 Mar 2021 18:12:50 +0000
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```
import networkx as nx
from networkx.testing import almost_equal

def weighted_G():
G = nx.Graph()
return G

class TestBetweennessCentrality:
def test_K5(self):
"""Betweenness centrality: K5"""
G = nx.complete_graph(5)
b = nx.betweenness_centrality(G, weight=None, normalized=False)
b_answer = {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}
for n in sorted(G):

def test_K5_endpoints(self):
"""Betweenness centrality: K5 endpoints"""
G = nx.complete_graph(5)
b = nx.betweenness_centrality(G, weight=None, normalized=False, endpoints=True)
b_answer = {0: 4.0, 1: 4.0, 2: 4.0, 3: 4.0, 4: 4.0}
for n in sorted(G):
# normalized = True case
b = nx.betweenness_centrality(G, weight=None, normalized=True, endpoints=True)
b_answer = {0: 0.4, 1: 0.4, 2: 0.4, 3: 0.4, 4: 0.4}
for n in sorted(G):

def test_P3_normalized(self):
"""Betweenness centrality: P3 normalized"""
G = nx.path_graph(3)
b = nx.betweenness_centrality(G, weight=None, normalized=True)
b_answer = {0: 0.0, 1: 1.0, 2: 0.0}
for n in sorted(G):

def test_P3(self):
"""Betweenness centrality: P3"""
G = nx.path_graph(3)
b_answer = {0: 0.0, 1: 1.0, 2: 0.0}
b = nx.betweenness_centrality(G, weight=None, normalized=False)
for n in sorted(G):

def test_sample_from_P3(self):
G = nx.path_graph(3)
b_answer = {0: 0.0, 1: 1.0, 2: 0.0}
b = nx.betweenness_centrality(G, k=3, weight=None, normalized=False, seed=1)
for n in sorted(G):
b = nx.betweenness_centrality(G, k=2, weight=None, normalized=False, seed=1)
# python versions give different results with same seed
b_approx1 = {0: 0.0, 1: 1.5, 2: 0.0}
b_approx2 = {0: 0.0, 1: 0.75, 2: 0.0}
for n in sorted(G):
assert b[n] in (b_approx1[n], b_approx2[n])

def test_P3_endpoints(self):
"""Betweenness centrality: P3 endpoints"""
G = nx.path_graph(3)
b_answer = {0: 2.0, 1: 3.0, 2: 2.0}
b = nx.betweenness_centrality(G, weight=None, normalized=False, endpoints=True)
for n in sorted(G):
# normalized = True case
b_answer = {0: 2 / 3, 1: 1.0, 2: 2 / 3}
b = nx.betweenness_centrality(G, weight=None, normalized=True, endpoints=True)
for n in sorted(G):

def test_krackhardt_kite_graph(self):
"""Betweenness centrality: Krackhardt kite graph"""
G = nx.krackhardt_kite_graph()
0: 1.667,
1: 1.667,
2: 0.000,
3: 7.333,
4: 0.000,
5: 16.667,
6: 16.667,
7: 28.000,
8: 16.000,
9: 0.000,
}
b = nx.betweenness_centrality(G, weight=None, normalized=False)
for n in sorted(G):

def test_krackhardt_kite_graph_normalized(self):
"""Betweenness centrality: Krackhardt kite graph normalized"""
G = nx.krackhardt_kite_graph()
0: 0.023,
1: 0.023,
2: 0.000,
3: 0.102,
4: 0.000,
5: 0.231,
6: 0.231,
7: 0.389,
8: 0.222,
9: 0.000,
}
b = nx.betweenness_centrality(G, weight=None, normalized=True)
for n in sorted(G):

def test_florentine_families_graph(self):
"""Betweenness centrality: Florentine families graph"""
G = nx.florentine_families_graph()
"Acciaiuoli": 0.000,
"Albizzi": 0.212,
"Bischeri": 0.104,
"Castellani": 0.055,
"Ginori": 0.000,
"Lamberteschi": 0.000,
"Medici": 0.522,
"Pazzi": 0.000,
"Peruzzi": 0.022,
"Ridolfi": 0.114,
"Salviati": 0.143,
"Strozzi": 0.103,
"Tornabuoni": 0.092,
}

b = nx.betweenness_centrality(G, weight=None, normalized=True)
for n in sorted(G):

def test_les_miserables_graph(self):
"""Betweenness centrality: Les Miserables graph"""
G = nx.les_miserables_graph()
"Napoleon": 0.000,
"Myriel": 0.177,
"MlleBaptistine": 0.000,
"MmeMagloire": 0.000,
"CountessDeLo": 0.000,
"Geborand": 0.000,
"Champtercier": 0.000,
"Cravatte": 0.000,
"Count": 0.000,
"OldMan": 0.000,
"Valjean": 0.570,
"Labarre": 0.000,
"Marguerite": 0.000,
"MmeDeR": 0.000,
"Isabeau": 0.000,
"Gervais": 0.000,
"Listolier": 0.000,
"Tholomyes": 0.041,
"Fameuil": 0.000,
"Blacheville": 0.000,
"Favourite": 0.000,
"Dahlia": 0.000,
"Zephine": 0.000,
"Fantine": 0.130,
"MmeThenardier": 0.029,
"Thenardier": 0.075,
"Cosette": 0.024,
"Javert": 0.054,
"Fauchelevent": 0.026,
"Bamatabois": 0.008,
"Perpetue": 0.000,
"Simplice": 0.009,
"Scaufflaire": 0.000,
"Woman1": 0.000,
"Judge": 0.000,
"Champmathieu": 0.000,
"Brevet": 0.000,
"Chenildieu": 0.000,
"Cochepaille": 0.000,
"Pontmercy": 0.007,
"Boulatruelle": 0.000,
"Eponine": 0.011,
"Anzelma": 0.000,
"Woman2": 0.000,
"MotherInnocent": 0.000,
"Gribier": 0.000,
"MmeBurgon": 0.026,
"Jondrette": 0.000,
"Gavroche": 0.165,
"Gillenormand": 0.020,
"Magnon": 0.000,
"MlleGillenormand": 0.048,
"MmePontmercy": 0.000,
"MlleVaubois": 0.000,
"LtGillenormand": 0.000,
"Marius": 0.132,
"BaronessT": 0.000,
"Mabeuf": 0.028,
"Enjolras": 0.043,
"Combeferre": 0.001,
"Prouvaire": 0.000,
"Feuilly": 0.001,
"Courfeyrac": 0.005,
"Bahorel": 0.002,
"Bossuet": 0.031,
"Joly": 0.002,
"Grantaire": 0.000,
"MotherPlutarch": 0.000,
"Gueulemer": 0.005,
"Babet": 0.005,
"Claquesous": 0.005,
"Montparnasse": 0.004,
"Toussaint": 0.000,
"Child1": 0.000,
"Child2": 0.000,
"Brujon": 0.000,
"MmeHucheloup": 0.000,
}

b = nx.betweenness_centrality(G, weight=None, normalized=True)
for n in sorted(G):

G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (2, 4), (4, 5), (3, 5)])
b_answer = {0: 1.667, 1: 1.667, 2: 6.667, 3: 6.667, 4: 1.667, 5: 1.667}
b = nx.betweenness_centrality(G, weight=None, normalized=False)
for n in sorted(G):

def test_disconnected_path(self):
"""Betweenness centrality: disconnected path"""
G = nx.Graph()
b_answer = {0: 0, 1: 1, 2: 0, 3: 0, 4: 2, 5: 2, 6: 0}
b = nx.betweenness_centrality(G, weight=None, normalized=False)
for n in sorted(G):

def test_disconnected_path_endpoints(self):
"""Betweenness centrality: disconnected path endpoints"""
G = nx.Graph()
b_answer = {0: 2, 1: 3, 2: 2, 3: 3, 4: 5, 5: 5, 6: 3}
b = nx.betweenness_centrality(G, weight=None, normalized=False, endpoints=True)
for n in sorted(G):
# normalized = True case
b = nx.betweenness_centrality(G, weight=None, normalized=True, endpoints=True)
for n in sorted(G):

def test_directed_path(self):
"""Betweenness centrality: directed path"""
G = nx.DiGraph()
b = nx.betweenness_centrality(G, weight=None, normalized=False)
b_answer = {0: 0.0, 1: 1.0, 2: 0.0}
for n in sorted(G):

def test_directed_path_normalized(self):
"""Betweenness centrality: directed path normalized"""
G = nx.DiGraph()
b = nx.betweenness_centrality(G, weight=None, normalized=True)
b_answer = {0: 0.0, 1: 0.5, 2: 0.0}
for n in sorted(G):

class TestWeightedBetweennessCentrality:
def test_K5(self):
"""Weighted betweenness centrality: K5"""
G = nx.complete_graph(5)
b = nx.betweenness_centrality(G, weight="weight", normalized=False)
b_answer = {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}
for n in sorted(G):

def test_P3_normalized(self):
"""Weighted betweenness centrality: P3 normalized"""
G = nx.path_graph(3)
b = nx.betweenness_centrality(G, weight="weight", normalized=True)
b_answer = {0: 0.0, 1: 1.0, 2: 0.0}
for n in sorted(G):

def test_P3(self):
"""Weighted betweenness centrality: P3"""
G = nx.path_graph(3)
b_answer = {0: 0.0, 1: 1.0, 2: 0.0}
b = nx.betweenness_centrality(G, weight="weight", normalized=False)
for n in sorted(G):

def test_krackhardt_kite_graph(self):
"""Weighted betweenness centrality: Krackhardt kite graph"""
G = nx.krackhardt_kite_graph()
0: 1.667,
1: 1.667,
2: 0.000,
3: 7.333,
4: 0.000,
5: 16.667,
6: 16.667,
7: 28.000,
8: 16.000,
9: 0.000,
}

b = nx.betweenness_centrality(G, weight="weight", normalized=False)

for n in sorted(G):

def test_krackhardt_kite_graph_normalized(self):
"""Weighted betweenness centrality:
Krackhardt kite graph normalized
"""
G = nx.krackhardt_kite_graph()
0: 0.023,
1: 0.023,
2: 0.000,
3: 0.102,
4: 0.000,
5: 0.231,
6: 0.231,
7: 0.389,
8: 0.222,
9: 0.000,
}
b = nx.betweenness_centrality(G, weight="weight", normalized=True)

for n in sorted(G):

def test_florentine_families_graph(self):
"""Weighted betweenness centrality:
Florentine families graph"""
G = nx.florentine_families_graph()
"Acciaiuoli": 0.000,
"Albizzi": 0.212,
"Bischeri": 0.104,
"Castellani": 0.055,
"Ginori": 0.000,
"Lamberteschi": 0.000,
"Medici": 0.522,
"Pazzi": 0.000,
"Peruzzi": 0.022,
"Ridolfi": 0.114,
"Salviati": 0.143,
"Strozzi": 0.103,
"Tornabuoni": 0.092,
}

b = nx.betweenness_centrality(G, weight="weight", normalized=True)
for n in sorted(G):

def test_les_miserables_graph(self):
"""Weighted betweenness centrality: Les Miserables graph"""
G = nx.les_miserables_graph()
"Napoleon": 0.000,
"Myriel": 0.177,
"MlleBaptistine": 0.000,
"MmeMagloire": 0.000,
"CountessDeLo": 0.000,
"Geborand": 0.000,
"Champtercier": 0.000,
"Cravatte": 0.000,
"Count": 0.000,
"OldMan": 0.000,
"Valjean": 0.454,
"Labarre": 0.000,
"Marguerite": 0.009,
"MmeDeR": 0.000,
"Isabeau": 0.000,
"Gervais": 0.000,
"Listolier": 0.000,
"Tholomyes": 0.066,
"Fameuil": 0.000,
"Blacheville": 0.000,
"Favourite": 0.000,
"Dahlia": 0.000,
"Zephine": 0.000,
"Fantine": 0.114,
"MmeThenardier": 0.046,
"Thenardier": 0.129,
"Cosette": 0.075,
"Javert": 0.193,
"Fauchelevent": 0.026,
"Bamatabois": 0.080,
"Perpetue": 0.000,
"Simplice": 0.001,
"Scaufflaire": 0.000,
"Woman1": 0.000,
"Judge": 0.000,
"Champmathieu": 0.000,
"Brevet": 0.000,
"Chenildieu": 0.000,
"Cochepaille": 0.000,
"Pontmercy": 0.023,
"Boulatruelle": 0.000,
"Eponine": 0.023,
"Anzelma": 0.000,
"Woman2": 0.000,
"MotherInnocent": 0.000,
"Gribier": 0.000,
"MmeBurgon": 0.026,
"Jondrette": 0.000,
"Gavroche": 0.285,
"Gillenormand": 0.024,
"Magnon": 0.005,
"MlleGillenormand": 0.036,
"MmePontmercy": 0.005,
"MlleVaubois": 0.000,
"LtGillenormand": 0.015,
"Marius": 0.072,
"BaronessT": 0.004,
"Mabeuf": 0.089,
"Enjolras": 0.003,
"Combeferre": 0.000,
"Prouvaire": 0.000,
"Feuilly": 0.004,
"Courfeyrac": 0.001,
"Bahorel": 0.007,
"Bossuet": 0.028,
"Joly": 0.000,
"Grantaire": 0.036,
"MotherPlutarch": 0.000,
"Gueulemer": 0.025,
"Babet": 0.015,
"Claquesous": 0.042,
"Montparnasse": 0.050,
"Toussaint": 0.011,
"Child1": 0.000,
"Child2": 0.000,
"Brujon": 0.002,
"MmeHucheloup": 0.034,
}

b = nx.betweenness_centrality(G, weight="weight", normalized=True)
for n in sorted(G):

G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (2, 4), (4, 5), (3, 5)])
b_answer = {0: 1.667, 1: 1.667, 2: 6.667, 3: 6.667, 4: 1.667, 5: 1.667}
b = nx.betweenness_centrality(G, weight="weight", normalized=False)
for n in sorted(G):

def test_G(self):
"""Weighted betweenness centrality: G"""
G = weighted_G()
b_answer = {0: 2.0, 1: 0.0, 2: 4.0, 3: 3.0, 4: 4.0, 5: 0.0}
b = nx.betweenness_centrality(G, weight="weight", normalized=False)
for n in sorted(G):

def test_G2(self):
"""Weighted betweenness centrality: G2"""
G = nx.DiGraph()
[
("s", "u", 10),
("s", "x", 5),
("u", "v", 1),
("u", "x", 2),
("v", "y", 1),
("x", "u", 3),
("x", "v", 5),
("x", "y", 2),
("y", "s", 7),
("y", "v", 6),
]
)

b_answer = {"y": 5.0, "x": 5.0, "s": 4.0, "u": 2.0, "v": 2.0}

b = nx.betweenness_centrality(G, weight="weight", normalized=False)
for n in sorted(G):

class TestEdgeBetweennessCentrality:
def test_K5(self):
"""Edge betweenness centrality: K5"""
G = nx.complete_graph(5)
b = nx.edge_betweenness_centrality(G, weight=None, normalized=False)
for n in sorted(G.edges()):

def test_normalized_K5(self):
"""Edge betweenness centrality: K5"""
G = nx.complete_graph(5)
b = nx.edge_betweenness_centrality(G, weight=None, normalized=True)
b_answer = dict.fromkeys(G.edges(), 1 / 10)
for n in sorted(G.edges()):

def test_C4(self):
"""Edge betweenness centrality: C4"""
G = nx.cycle_graph(4)
b = nx.edge_betweenness_centrality(G, weight=None, normalized=True)
b_answer = {(0, 1): 2, (0, 3): 2, (1, 2): 2, (2, 3): 2}
for n in sorted(G.edges()):

def test_P4(self):
"""Edge betweenness centrality: P4"""
G = nx.path_graph(4)
b = nx.edge_betweenness_centrality(G, weight=None, normalized=False)
b_answer = {(0, 1): 3, (1, 2): 4, (2, 3): 3}
for n in sorted(G.edges()):

def test_normalized_P4(self):
"""Edge betweenness centrality: P4"""
G = nx.path_graph(4)
b = nx.edge_betweenness_centrality(G, weight=None, normalized=True)
b_answer = {(0, 1): 3, (1, 2): 4, (2, 3): 3}
for n in sorted(G.edges()):

def test_balanced_tree(self):
"""Edge betweenness centrality: balanced tree"""
G = nx.balanced_tree(r=2, h=2)
b = nx.edge_betweenness_centrality(G, weight=None, normalized=False)
b_answer = {(0, 1): 12, (0, 2): 12, (1, 3): 6, (1, 4): 6, (2, 5): 6, (2, 6): 6}
for n in sorted(G.edges()):

class TestWeightedEdgeBetweennessCentrality:
def test_K5(self):
"""Edge betweenness centrality: K5"""
G = nx.complete_graph(5)
b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False)
for n in sorted(G.edges()):

def test_C4(self):
"""Edge betweenness centrality: C4"""
G = nx.cycle_graph(4)
b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False)
b_answer = {(0, 1): 2, (0, 3): 2, (1, 2): 2, (2, 3): 2}
for n in sorted(G.edges()):

def test_P4(self):
"""Edge betweenness centrality: P4"""
G = nx.path_graph(4)
b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False)
b_answer = {(0, 1): 3, (1, 2): 4, (2, 3): 3}
for n in sorted(G.edges()):

def test_balanced_tree(self):
"""Edge betweenness centrality: balanced tree"""
G = nx.balanced_tree(r=2, h=2)
b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False)
b_answer = {(0, 1): 12, (0, 2): 12, (1, 3): 6, (1, 4): 6, (2, 5): 6, (2, 6): 6}
for n in sorted(G.edges()):

def test_weighted_graph(self):
eList = [
(0, 1, 5),
(0, 2, 4),
(0, 3, 3),
(0, 4, 2),
(1, 2, 4),
(1, 3, 1),
(1, 4, 3),
(2, 4, 5),
(3, 4, 4),
]
G = nx.Graph()
b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False)
(0, 1): 0.0,
(0, 2): 1.0,
(0, 3): 2.0,
(0, 4): 1.0,
(1, 2): 2.0,
(1, 3): 3.5,
(1, 4): 1.5,
(2, 4): 1.0,
(3, 4): 0.5,
}
for n in sorted(G.edges()):

def test_normalized_weighted_graph(self):
eList = [
(0, 1, 5),
(0, 2, 4),
(0, 3, 3),
(0, 4, 2),
(1, 2, 4),
(1, 3, 1),
(1, 4, 3),
(2, 4, 5),
(3, 4, 4),
]
G = nx.Graph()
b = nx.edge_betweenness_centrality(G, weight="weight", normalized=True)
(0, 1): 0.0,
(0, 2): 1.0,
(0, 3): 2.0,
(0, 4): 1.0,
(1, 2): 2.0,
(1, 3): 3.5,
(1, 4): 1.5,
(2, 4): 1.0,
(3, 4): 0.5,
}
norm = len(G) * (len(G) - 1) / 2
for n in sorted(G.edges()):