def test_pagerank(self):
G = self.G
p = nx.pagerank(G, alpha=0.9, tol=1.0e-08)
for n in G:
assert almost_equal(p[n], G.pagerank[n], places=4)
nstart = dict((n, random.random()) for n in G)
p = nx.pagerank(G, alpha=0.9, tol=1.0e-08, nstart=nstart)
for n in G:
assert almost_equal(p[n], G.pagerank[n], places=4)
def test_incomplete_personalization(self):
G = nx.complete_graph(4)
personalize = {3: 1}
answer = {
0: 0.22077931820379187,
1: 0.22077931820379187,
2: 0.22077931820379187,
3: 0.3376620453886241,
}
p = nx.pagerank(G, alpha=0.85, personalization=personalize)
for n in G:
assert almost_equal(p[n], answer[n], places=4)
def test_personalization(self):
G = nx.complete_graph(4)
personalize = {0: 1, 1: 1, 2: 4, 3: 4}
answer = {
0: 0.23246732615667579,
1: 0.23246732615667579,
2: 0.267532673843324,
3: 0.2675326738433241,
}
p = nx.pagerank(G, alpha=0.85, personalization=personalize)
for n in G:
assert almost_equal(p[n], answer[n], places=4)
def test_pagerank_max_iter(self):
with pytest.raises(nx.PowerIterationFailedConvergence):
nx.pagerank(self.G, max_iter=0)
def test_empty(self):
G = nx.Graph()
assert nx.pagerank(G) == {}
assert nx.pagerank_numpy(G) == {}
assert nx.google_matrix(G).shape == (0, 0)
def test_dangling_pagerank(self):
pr = nx.pagerank(self.G, dangling=self.dangling_edges)
for n in self.G:
assert almost_equal(pr[n], self.G.dangling_pagerank[n], places=4)