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from nose.tools import assert_equal
import networkx as nx
from networkx.algorithms.approximation import average_clustering
# This approximation has to be be exact in regular graphs
# with no triangles or with all possible triangles.
def test_petersen():
# Actual coefficient is 0
G = nx.petersen_graph()
assert_equal(average_clustering(G, trials=int(len(G)/2)),
nx.average_clustering(G))
def test_tetrahedral():
# Actual coefficient is 1
G = nx.tetrahedral_graph()
assert_equal(average_clustering(G, trials=int(len(G)/2)),
nx.average_clustering(G))
def test_dodecahedral():
# Actual coefficient is 0
G = nx.dodecahedral_graph()
assert_equal(average_clustering(G, trials=int(len(G)/2)),
nx.average_clustering(G))
def test_empty():
G = nx.empty_graph(5)
assert_equal(average_clustering(G, trials=int(len(G)/2)), 0)
def test_complete():
G = nx.complete_graph(5)
assert_equal(average_clustering(G, trials=int(len(G)/2)), 1)
G = nx.complete_graph(7)
assert_equal(average_clustering(G, trials=int(len(G)/2)), 1)
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