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import networkx as nx
def small_ego_G():
"""The sample network from https://arxiv.org/pdf/1310.6753v1.pdf"""
edges = [
("a", "b"),
("a", "c"),
("b", "c"),
("b", "d"),
("b", "e"),
("b", "f"),
("c", "d"),
("c", "f"),
("c", "h"),
("d", "f"),
("e", "f"),
("f", "h"),
("h", "j"),
("h", "k"),
("i", "j"),
("i", "k"),
("j", "k"),
("u", "a"),
("u", "b"),
("u", "c"),
("u", "d"),
("u", "e"),
("u", "f"),
("u", "g"),
("u", "h"),
("u", "i"),
("u", "j"),
("u", "k"),
]
G = nx.Graph()
G.add_edges_from(edges)
return G
class TestDispersion:
def test_article(self):
"""our algorithm matches article's"""
G = small_ego_G()
disp_uh = nx.dispersion(G, "u", "h", normalized=False)
disp_ub = nx.dispersion(G, "u", "b", normalized=False)
assert disp_uh == 4
assert disp_ub == 1
def test_results_length(self):
"""there is a result for every node"""
G = small_ego_G()
disp = nx.dispersion(G)
disp_Gu = nx.dispersion(G, "u")
disp_uv = nx.dispersion(G, "u", "h")
assert len(disp) == len(G)
assert len(disp_Gu) == len(G) - 1
assert isinstance(disp_uv, float)
def test_dispersion_v_only(self):
G = small_ego_G()
disp_G_h = nx.dispersion(G, v="h", normalized=False)
disp_G_h_normalized = nx.dispersion(G, v="h", normalized=True)
assert disp_G_h == {"c": 0, "f": 0, "j": 0, "k": 0, "u": 4}
assert disp_G_h_normalized == {"c": 0.0, "f": 0.0, "j": 0.0, "k": 0.0, "u": 1.0}
def test_impossible_things(self):
G = nx.karate_club_graph()
disp = nx.dispersion(G)
for u in disp:
for v in disp[u]:
assert disp[u][v] >= 0
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