desc stringlengths 3 26.7k | decl stringlengths 11 7.89k | bodies stringlengths 8 553k |
|---|---|---|
'Initialize TimeRespectingGraphMatcher.
G1 and G2 should be nx.Graph or nx.MultiGraph instances.
Examples
To create a TimeRespectingGraphMatcher which checks for
syntactic and semantic feasibility:
>>> from networkx.algorithms import isomorphism
>>> G1 = nx.Graph(nx.path_graph(4, create_using=nx.Graph()))
>>> G2 = nx.G... | def __init__(self, G1, G2, temporal_attribute_name, delta):
| self.temporal_attribute_name = temporal_attribute_name
self.delta = delta
super(TimeRespectingGraphMatcher, self).__init__(G1, G2)
|
'Edges one hop out from a node in the mapping should be
time-respecting with respect to each other.'
| def one_hop(self, Gx, Gx_node, neighbors):
| dates = []
for n in neighbors:
if (type(Gx) == type(nx.Graph())):
dates.append(Gx[Gx_node][n][self.temporal_attribute_name])
else:
for edge in Gx[Gx_node][n].values():
dates.append(edge[self.temporal_attribute_name])
if any(((x is None) for x in dates)... |
'Paths of length 2 from Gx_node should be time-respecting.'
| def two_hop(self, Gx, core_x, Gx_node, neighbors):
| return all((self.one_hop(Gx, v, ([n for n in Gx[v] if (n in core_x)] + [Gx_node])) for v in neighbors))
|
'Returns True if adding (G1_node, G2_node) is semantically
feasible.
Any subclass which redefines semantic_feasibility() must
maintain the self.tests if needed, to keep the match() method
functional. Implementations should consider multigraphs.'
| def semantic_feasibility(self, G1_node, G2_node):
| neighbors = [n for n in self.G1[G1_node] if (n in self.core_1)]
if (not self.one_hop(self.G1, G1_node, neighbors)):
return False
if (not self.two_hop(self.G1, self.core_1, G1_node, neighbors)):
return False
return True
|
'Initialize TimeRespectingDiGraphMatcher.
G1 and G2 should be nx.DiGraph or nx.MultiDiGraph instances.
Examples
To create a TimeRespectingDiGraphMatcher which checks for
syntactic and semantic feasibility:
>>> from networkx.algorithms import isomorphism
>>> G1 = nx.DiGraph(nx.path_graph(4, create_using=nx.DiGraph()))
>... | def __init__(self, G1, G2, temporal_attribute_name, delta):
| self.temporal_attribute_name = temporal_attribute_name
self.delta = delta
super(TimeRespectingDiGraphMatcher, self).__init__(G1, G2)
|
'Get the dates of edges from predecessors.'
| def get_pred_dates(self, Gx, Gx_node, core_x, pred):
| pred_dates = []
if (type(Gx) == type(nx.DiGraph())):
for n in pred:
pred_dates.append(Gx[n][Gx_node][self.temporal_attribute_name])
else:
for n in pred:
for edge in Gx[n][Gx_node].values():
pred_dates.append(edge[self.temporal_attribute_name])
retu... |
'Get the dates of edges to successors.'
| def get_succ_dates(self, Gx, Gx_node, core_x, succ):
| succ_dates = []
if (type(Gx) == type(nx.DiGraph())):
for n in succ:
succ_dates.append(Gx[Gx_node][n][self.temporal_attribute_name])
else:
for n in succ:
for edge in Gx[Gx_node][n].values():
succ_dates.append(edge[self.temporal_attribute_name])
retu... |
'The ego node.'
| def one_hop(self, Gx, Gx_node, core_x, pred, succ):
| pred_dates = self.get_pred_dates(Gx, Gx_node, core_x, pred)
succ_dates = self.get_succ_dates(Gx, Gx_node, core_x, succ)
return (self.test_one(pred_dates, succ_dates) and self.test_two(pred_dates, succ_dates))
|
'The predeccessors of the ego node.'
| def two_hop_pred(self, Gx, Gx_node, core_x, pred):
| return all((self.one_hop(Gx, p, core_x, self.preds(Gx, core_x, p), self.succs(Gx, core_x, p, Gx_node)) for p in pred))
|
'The successors of the ego node.'
| def two_hop_succ(self, Gx, Gx_node, core_x, succ):
| return all((self.one_hop(Gx, s, core_x, self.preds(Gx, core_x, s, Gx_node), self.succs(Gx, core_x, s)) for s in succ))
|
'Edges one hop out from Gx_node in the mapping should be
time-respecting with respect to each other, regardless of
direction.'
| def test_one(self, pred_dates, succ_dates):
| time_respecting = True
dates = (pred_dates + succ_dates)
if any(((x is None) for x in dates)):
raise ValueError('Date or datetime not supplied for at least one edge.')
dates.sort()
if ((0 < len(dates)) and (not ((dates[(-1)] - dates[0]) <= self.delta))):
ti... |
'Edges from a dual Gx_node in the mapping should be ordered in
a time-respecting manner.'
| def test_two(self, pred_dates, succ_dates):
| time_respecting = True
pred_dates.sort()
succ_dates.sort()
if ((0 < len(succ_dates)) and (0 < len(pred_dates)) and (succ_dates[0] < pred_dates[(-1)])):
time_respecting = False
return time_respecting
|
'Returns True if adding (G1_node, G2_node) is semantically
feasible.
Any subclass which redefines semantic_feasibility() must
maintain the self.tests if needed, to keep the match() method
functional. Implementations should consider multigraphs.'
| def semantic_feasibility(self, G1_node, G2_node):
| (pred, succ) = ([n for n in self.G1.predecessors(G1_node) if (n in self.core_1)], [n for n in self.G1.successors(G1_node) if (n in self.core_1)])
if (not self.one_hop(self.G1, G1_node, self.core_1, pred, succ)):
return False
if (not self.two_hop_pred(self.G1, G1_node, self.core_1, pred)):
re... |
'Initialize graph matcher.
Parameters
G1, G2: graph
The graphs to be tested.
node_match: callable
A function that returns True iff node n1 in G1 and n2 in G2
should be considered equal during the isomorphism test. The
function will be called like::
node_match(G1.node[n1], G2.node[n2])
That is, the function will receive... | def __init__(self, G1, G2, node_match=None, edge_match=None):
| vf2.GraphMatcher.__init__(self, G1, G2)
self.node_match = node_match
self.edge_match = edge_match
self.G1_adj = self.G1.adj
self.G2_adj = self.G2.adj
|
'Initialize graph matcher.
Parameters
G1, G2 : graph
The graphs to be tested.
node_match : callable
A function that returns True iff node n1 in G1 and n2 in G2
should be considered equal during the isomorphism test. The
function will be called like::
node_match(G1.node[n1], G2.node[n2])
That is, the function will recei... | def __init__(self, G1, G2, node_match=None, edge_match=None):
| vf2.DiGraphMatcher.__init__(self, G1, G2)
self.node_match = node_match
self.edge_match = edge_match
self.G1_adj = self.G1.adj
self.G2_adj = self.G2.adj
|
'Returns True if mapping G1_node to G2_node is semantically feasible.'
| def semantic_feasibility(self, G1_node, G2_node):
| feasible = _semantic_feasibility(self, G1_node, G2_node)
if (not feasible):
return False
self.G1_adj = self.G1.pred
self.G2_adj = self.G2.pred
feasible = _semantic_feasibility(self, G1_node, G2_node)
self.G1_adj = self.G1.adj
self.G2_adj = self.G2.adj
return feasible
|
'Tests that the google_matrix doesn\'t change except for the dangling
nodes.'
| def test_dangling_matrix(self):
| G = self.G
dangling = self.dangling_edges
dangling_sum = float(sum(dangling.values()))
M1 = networkx.google_matrix(G, personalization=dangling)
M2 = networkx.google_matrix(G, personalization=dangling, dangling=dangling)
for i in range(len(G)):
for j in range(len(G)):
if ((i =... |
'Tests that a poor partition has a low performance measure.'
| def test_bad_partition(self):
| G = barbell_graph(3, 0)
partition = [{0, 1, 4}, {2, 3, 5}]
assert_almost_equal((8 / 15), performance(G, partition))
|
'Tests that a good partition has a high performance measure.'
| def test_good_partition(self):
| G = barbell_graph(3, 0)
partition = [{0, 1, 2}, {3, 4, 5}]
assert_almost_equal((14 / 15), performance(G, partition))
|
'Tests that a poor partition has a low coverage measure.'
| def test_bad_partition(self):
| G = barbell_graph(3, 0)
partition = [{0, 1, 4}, {2, 3, 5}]
assert_almost_equal((3 / 7), coverage(G, partition))
|
'Tests that a good partition has a high coverage measure.'
| def test_good_partition(self):
| G = barbell_graph(3, 0)
partition = [{0, 1, 2}, {3, 4, 5}]
assert_almost_equal((6 / 7), coverage(G, partition))
|
'Checks that the communities computed from the given graph ``G``
using the :func:`~networkx.asyn_lpa_communities` function match
the set of nodes given in ``expected``.
``expected`` must be a :class:`set` of :class:`frozenset`
instances, each element of which is a node in the graph.'
| def _check_communities(self, G, expected):
| communities = asyn_lpa_communities(G)
result = {frozenset(c) for c in communities}
assert_equal(result, expected)
|
'Eigenvector centrality: K5'
| def test_K5(self):
| G = nx.complete_graph(5)
b = nx.eigenvector_centrality(G)
v = math.sqrt((1 / 5.0))
b_answer = dict.fromkeys(G, v)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
nstart = dict([(n, 1) for n in G])
b = nx.eigenvector_centrality(G, nstart=nstart)
for n in sorted(G):
... |
'Eigenvector centrality: P3'
| def test_P3(self):
| G = nx.path_graph(3)
b_answer = {0: 0.5, 1: 0.7071, 2: 0.5}
b = nx.eigenvector_centrality_numpy(G)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n], places=4)
b = nx.eigenvector_centrality(G)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n], places=4)
|
'Eigenvector centrality: P3'
| def test_P3_unweighted(self):
| G = nx.path_graph(3)
b_answer = {0: 0.5, 1: 0.7071, 2: 0.5}
b = nx.eigenvector_centrality_numpy(G, weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n], places=4)
|
'Closeness centrality: K4'
| def test_K4(self):
| G = nx.complete_graph(4)
b = nx.current_flow_closeness_centrality(G)
b_answer = {0: (2.0 / 3), 1: (2.0 / 3), 2: (2.0 / 3), 3: (2.0 / 3)}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Closeness centrality: P4'
| def test_P4(self):
| G = nx.path_graph(4)
b = nx.current_flow_closeness_centrality(G)
b_answer = {0: (1.0 / 6), 1: (1.0 / 4), 2: (1.0 / 4), 3: (1.0 / 6)}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Closeness centrality: star'
| def test_star(self):
| G = nx.Graph()
nx.add_star(G, ['a', 'b', 'c', 'd'])
b = nx.current_flow_closeness_centrality(G)
b_answer = {'a': (1.0 / 3), 'b': (0.6 / 3), 'c': (0.6 / 3), 'd': (0.6 / 3)}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: K4'
| def test_K4_normalized(self):
| G = nx.complete_graph(4)
b = nx.current_flow_betweenness_centrality_subset(G, list(G), list(G), normalized=True)
b_answer = nx.current_flow_betweenness_centrality(G, normalized=True)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: K4'
| def test_K4(self):
| G = nx.complete_graph(4)
b = nx.current_flow_betweenness_centrality_subset(G, list(G), list(G), normalized=True)
b_answer = nx.current_flow_betweenness_centrality(G, normalized=True)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
G.add_edge(0, 1, weight=0.5, other=0.3)
b = nx... |
'Betweenness centrality: P4 normalized'
| def test_P4_normalized(self):
| G = nx.path_graph(4)
b = nx.current_flow_betweenness_centrality_subset(G, list(G), list(G), normalized=True)
b_answer = nx.current_flow_betweenness_centrality(G, normalized=True)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: P4'
| def test_P4(self):
| G = nx.path_graph(4)
b = nx.current_flow_betweenness_centrality_subset(G, list(G), list(G), normalized=True)
b_answer = nx.current_flow_betweenness_centrality(G, normalized=True)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: star'
| def test_star(self):
| G = nx.Graph()
nx.add_star(G, ['a', 'b', 'c', 'd'])
b = nx.current_flow_betweenness_centrality_subset(G, list(G), list(G), normalized=True)
b_answer = nx.current_flow_betweenness_centrality(G, normalized=True)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: K4'
| def test_K4_normalized(self):
| G = nx.complete_graph(4)
b = edge_current_flow_subset(G, list(G), list(G), normalized=True)
b_answer = edge_current_flow(G, normalized=True)
for ((s, t), v1) in b_answer.items():
v2 = b.get((s, t), b.get((t, s)))
assert_almost_equal(v1, v2)
|
'Betweenness centrality: K4'
| def test_K4(self):
| G = nx.complete_graph(4)
b = edge_current_flow_subset(G, list(G), list(G), normalized=False)
b_answer = edge_current_flow(G, normalized=False)
for ((s, t), v1) in b_answer.items():
v2 = b.get((s, t), b.get((t, s)))
assert_almost_equal(v1, v2)
G.add_edge(0, 1, weight=0.5, other=0.3)
... |
'Edge betweenness centrality: C4'
| def test_C4(self):
| G = nx.cycle_graph(4)
b = edge_current_flow_subset(G, list(G), list(G), normalized=True)
b_answer = edge_current_flow(G, normalized=True)
for ((s, t), v1) in b_answer.items():
v2 = b.get((s, t), b.get((t, s)))
assert_almost_equal(v1, v2)
|
'Edge betweenness centrality: P4'
| def test_P4(self):
| G = nx.path_graph(4)
b = edge_current_flow_subset(G, list(G), list(G), normalized=True)
b_answer = edge_current_flow(G, normalized=True)
for ((s, t), v1) in b_answer.items():
v2 = b.get((s, t), b.get((t, s)))
assert_almost_equal(v1, v2)
|
'Betweenness centrality: K5'
| def test_K5(self):
| G = nx.complete_graph(5)
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[1, 3], weight=None)
b_answer = {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: P5 directed'
| def test_P5_directed(self):
| G = nx.DiGraph()
nx.add_path(G, range(5))
b_answer = {0: 0, 1: 1, 2: 1, 3: 0, 4: 0, 5: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3], weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: P5'
| def test_P5(self):
| G = nx.Graph()
nx.add_path(G, range(5))
b_answer = {0: 0, 1: 0.5, 2: 0.5, 3: 0, 4: 0, 5: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3], weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: P5 multiple target'
| def test_P5_multiple_target(self):
| G = nx.Graph()
nx.add_path(G, range(5))
b_answer = {0: 0, 1: 1, 2: 1, 3: 0.5, 4: 0, 5: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3, 4], weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: box'
| def test_box(self):
| G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
b_answer = {0: 0, 1: 0.25, 2: 0.25, 3: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3], weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: box and path'
| def test_box_and_path(self):
| G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (3, 4), (4, 5)])
b_answer = {0: 0, 1: 0.5, 2: 0.5, 3: 0.5, 4: 0, 5: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3, 4], weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: box and path multiple target'
| def test_box_and_path2(self):
| G = nx.Graph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (1, 20), (20, 3), (3, 4)])
b_answer = {0: 0, 1: 1.0, 2: 0.5, 20: 0.5, 3: 0.5, 4: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3, 4], weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: K5'
| def test_K5(self):
| G = nx.complete_graph(5)
b = nx.betweenness_centrality_source(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):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: P3'
| def test_P3(self):
| G = nx.path_graph(3)
b_answer = {0: 0.0, 1: 1.0, 2: 0.0}
b = nx.betweenness_centrality_source(G, weight=None, normalized=True)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Edge betweenness centrality: K5'
| def test_K5(self):
| G = nx.complete_graph(5)
b = nx.edge_betweenness_centrality_subset(G, sources=[0], targets=[1, 3], weight=None)
b_answer = dict.fromkeys(G.edges(), 0)
b_answer[(0, 3)] = b_answer[(0, 1)] = 0.5
for n in sorted(G.edges()):
assert_almost_equal(b[n], b_answer[n])
|
'Edge betweenness centrality: P5 directed'
| def test_P5_directed(self):
| G = nx.DiGraph()
nx.add_path(G, range(5))
b_answer = dict.fromkeys(G.edges(), 0)
b_answer[(0, 1)] = b_answer[(1, 2)] = b_answer[(2, 3)] = 1
b = nx.edge_betweenness_centrality_subset(G, sources=[0], targets=[3], weight=None)
for n in sorted(G.edges()):
assert_almost_equal(b[n], b_answer[n... |
'Edge betweenness centrality: P5'
| def test_P5(self):
| G = nx.Graph()
nx.add_path(G, range(5))
b_answer = dict.fromkeys(G.edges(), 0)
b_answer[(0, 1)] = b_answer[(1, 2)] = b_answer[(2, 3)] = 0.5
b = nx.edge_betweenness_centrality_subset(G, sources=[0], targets=[3], weight=None)
for n in sorted(G.edges()):
assert_almost_equal(b[n], b_answer[n... |
'Edge betweenness centrality: P5 multiple target'
| def test_P5_multiple_target(self):
| G = nx.Graph()
nx.add_path(G, range(5))
b_answer = dict.fromkeys(G.edges(), 0)
b_answer[(0, 1)] = b_answer[(1, 2)] = b_answer[(2, 3)] = 1
b_answer[(3, 4)] = 0.5
b = nx.edge_betweenness_centrality_subset(G, sources=[0], targets=[3, 4], weight=None)
for n in sorted(G.edges()):
assert_a... |
'Edge etweenness centrality: box'
| def test_box(self):
| G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
b_answer = dict.fromkeys(G.edges(), 0)
b_answer[(0, 1)] = b_answer[(0, 2)] = 0.25
b_answer[(1, 3)] = b_answer[(2, 3)] = 0.25
b = nx.edge_betweenness_centrality_subset(G, sources=[0], targets=[3], weight=None)
for n in sorted(G... |
'Edge etweenness centrality: box and path'
| def test_box_and_path(self):
| G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (3, 4), (4, 5)])
b_answer = dict.fromkeys(G.edges(), 0)
b_answer[(0, 1)] = b_answer[(0, 2)] = 0.5
b_answer[(1, 3)] = b_answer[(2, 3)] = 0.5
b_answer[(3, 4)] = 0.5
b = nx.edge_betweenness_centrality_subset(G, sources=[0], target... |
'Edge betweenness centrality: box and path multiple target'
| def test_box_and_path2(self):
| G = nx.Graph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (1, 20), (20, 3), (3, 4)])
b_answer = dict.fromkeys(G.edges(), 0)
b_answer[(0, 1)] = 1.0
b_answer[(1, 20)] = b_answer[(3, 20)] = 0.5
b_answer[(1, 2)] = b_answer[(2, 3)] = 0.5
b_answer[(3, 4)] = 0.5
b = nx.edge_betweenness_centralit... |
'Katz centrality: K5'
| def test_K5(self):
| G = nx.complete_graph(5)
alpha = 0.1
b = nx.katz_centrality(G, alpha)
v = math.sqrt((1 / 5.0))
b_answer = dict.fromkeys(G, v)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
nstart = dict([(n, 1) for n in G])
b = nx.katz_centrality(G, alpha, nstart=nstart)
for n in... |
'Katz centrality: P3'
| def test_P3(self):
| alpha = 0.1
G = nx.path_graph(3)
b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449, 2: 0.5598852584152162}
b = nx.katz_centrality(G, alpha)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n], places=4)
|
'Katz centrality: K5'
| def test_K5(self):
| G = nx.complete_graph(5)
alpha = 0.1
b = nx.katz_centrality(G, alpha)
v = math.sqrt((1 / 5.0))
b_answer = dict.fromkeys(G, v)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
nstart = dict([(n, 1) for n in G])
b = nx.eigenvector_centrality_numpy(G)
for n in sorted(G... |
'Katz centrality: P3'
| def test_P3(self):
| alpha = 0.1
G = nx.path_graph(3)
b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449, 2: 0.5598852584152162}
b = nx.katz_centrality_numpy(G, alpha)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n], places=4)
|
'Katz centrality: K5'
| def test_K5_unweighted(self):
| G = nx.complete_graph(5)
alpha = 0.1
b = nx.katz_centrality(G, alpha, weight=None)
v = math.sqrt((1 / 5.0))
b_answer = dict.fromkeys(G, v)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
nstart = dict([(n, 1) for n in G])
b = nx.eigenvector_centrality_numpy(G, weight=N... |
'Katz centrality: P3'
| def test_P3_unweighted(self):
| alpha = 0.1
G = nx.path_graph(3)
b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449, 2: 0.5598852584152162}
b = nx.katz_centrality_numpy(G, alpha, weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n], places=4)
|
'our algorithm matches article\'s'
| def test_article(self):
| 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)
|
'there is a result for every node'
| def test_results_length(self):
| 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 (type(disp_uv) is float)
|
'Betweenness centrality: K5'
| def test_K5(self):
| 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):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: K5 endpoints'
| def test_K5_endpoints(self):
| 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):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: P3 normalized'
| def test_P3_normalized(self):
| 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):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: P3'
| def test_P3(self):
| 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):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: P3 endpoints'
| def test_P3_endpoints(self):
| 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):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: Krackhardt kite graph'
| def test_krackhardt_kite_graph(self):
| G = nx.krackhardt_kite_graph()
b_answer = {0: 1.667, 1: 1.667, 2: 0.0, 3: 7.333, 4: 0.0, 5: 16.667, 6: 16.667, 7: 28.0, 8: 16.0, 9: 0.0}
for b in b_answer:
b_answer[b] /= 2.0
b = nx.betweenness_centrality(G, weight=None, normalized=False)
for n in sorted(G):
assert_almost_equal(b[n],... |
'Betweenness centrality: Krackhardt kite graph normalized'
| def test_krackhardt_kite_graph_normalized(self):
| G = nx.krackhardt_kite_graph()
b_answer = {0: 0.023, 1: 0.023, 2: 0.0, 3: 0.102, 4: 0.0, 5: 0.231, 6: 0.231, 7: 0.389, 8: 0.222, 9: 0.0}
b = nx.betweenness_centrality(G, weight=None, normalized=True)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n], places=3)
|
'Betweenness centrality: Florentine families graph'
| def test_florentine_families_graph(self):
| G = nx.florentine_families_graph()
b_answer = {'Acciaiuoli': 0.0, 'Albizzi': 0.212, 'Barbadori': 0.093, 'Bischeri': 0.104, 'Castellani': 0.055, 'Ginori': 0.0, 'Guadagni': 0.255, 'Lamberteschi': 0.0, 'Medici': 0.522, 'Pazzi': 0.0, 'Peruzzi': 0.022, 'Ridolfi': 0.114, 'Salviati': 0.143, 'Strozzi': 0.103, 'Tornabuo... |
'Betweenness centrality: Ladder graph'
| def test_ladder_graph(self):
| G = nx.Graph()
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}
for b in b_answer:
b_answer[b] /= 2.0
b = nx.betweenness_centrality(G, weight=None, normalized=False)
for n in sorted(G):
... |
'Betweenness centrality: disconnected path'
| def test_disconnected_path(self):
| G = nx.Graph()
nx.add_path(G, [0, 1, 2])
nx.add_path(G, [3, 4, 5, 6])
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):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: disconnected path endpoints'
| def test_disconnected_path_endpoints(self):
| G = nx.Graph()
nx.add_path(G, [0, 1, 2])
nx.add_path(G, [3, 4, 5, 6])
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):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: directed path'
| def test_directed_path(self):
| G = nx.DiGraph()
nx.add_path(G, [0, 1, 2])
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):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: directed path normalized'
| def test_directed_path_normalized(self):
| G = nx.DiGraph()
nx.add_path(G, [0, 1, 2])
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):
assert_almost_equal(b[n], b_answer[n])
|
'Weighted betweenness centrality: K5'
| def test_K5(self):
| 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):
assert_almost_equal(b[n], b_answer[n])
|
'Weighted betweenness centrality: P3 normalized'
| def test_P3_normalized(self):
| 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):
assert_almost_equal(b[n], b_answer[n])
|
'Weighted betweenness centrality: P3'
| def test_P3(self):
| 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):
assert_almost_equal(b[n], b_answer[n])
|
'Weighted betweenness centrality: Krackhardt kite graph'
| def test_krackhardt_kite_graph(self):
| G = nx.krackhardt_kite_graph()
b_answer = {0: 1.667, 1: 1.667, 2: 0.0, 3: 7.333, 4: 0.0, 5: 16.667, 6: 16.667, 7: 28.0, 8: 16.0, 9: 0.0}
for b in b_answer:
b_answer[b] /= 2.0
b = nx.betweenness_centrality(G, weight='weight', normalized=False)
for n in sorted(G):
assert_almost_equal(b... |
'Weighted betweenness centrality:
Krackhardt kite graph normalized'
| def test_krackhardt_kite_graph_normalized(self):
| G = nx.krackhardt_kite_graph()
b_answer = {0: 0.023, 1: 0.023, 2: 0.0, 3: 0.102, 4: 0.0, 5: 0.231, 6: 0.231, 7: 0.389, 8: 0.222, 9: 0.0}
b = nx.betweenness_centrality(G, weight='weight', normalized=True)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n], places=3)
|
'Weighted betweenness centrality:
Florentine families graph'
| def test_florentine_families_graph(self):
| G = nx.florentine_families_graph()
b_answer = {'Acciaiuoli': 0.0, 'Albizzi': 0.212, 'Barbadori': 0.093, 'Bischeri': 0.104, 'Castellani': 0.055, 'Ginori': 0.0, 'Guadagni': 0.255, 'Lamberteschi': 0.0, 'Medici': 0.522, 'Pazzi': 0.0, 'Peruzzi': 0.022, 'Ridolfi': 0.114, 'Salviati': 0.143, 'Strozzi': 0.103, 'Tornabuo... |
'Weighted betweenness centrality: Ladder graph'
| def test_ladder_graph(self):
| G = nx.Graph()
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}
for b in b_answer:
b_answer[b] /= 2.0
b = nx.betweenness_centrality(G, weight='weight', normalized=False)
for n in sorted(G):
... |
'Weighted betweenness centrality: G'
| def test_G(self):
| 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):
assert_almost_equal(b[n], b_answer[n])
|
'Weighted betweenness centrality: G2'
| def test_G2(self):
| G = nx.DiGraph()
G.add_weighted_edges_from([('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='weigh... |
'Edge betweenness centrality: K5'
| def test_K5(self):
| G = nx.complete_graph(5)
b = nx.edge_betweenness_centrality(G, weight=None, normalized=False)
b_answer = dict.fromkeys(G.edges(), 1)
for n in sorted(G.edges()):
assert_almost_equal(b[n], b_answer[n])
|
'Edge betweenness centrality: K5'
| def test_normalized_K5(self):
| G = nx.complete_graph(5)
b = nx.edge_betweenness_centrality(G, weight=None, normalized=True)
b_answer = dict.fromkeys(G.edges(), (1 / 10.0))
for n in sorted(G.edges()):
assert_almost_equal(b[n], b_answer[n])
|
'Edge betweenness centrality: C4'
| def test_C4(self):
| 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()):
assert_almost_equal(b[n], (b_answer[n] / 6.0))
|
'Edge betweenness centrality: P4'
| def test_P4(self):
| 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()):
assert_almost_equal(b[n], b_answer[n])
|
'Edge betweenness centrality: P4'
| def test_normalized_P4(self):
| 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()):
assert_almost_equal(b[n], (b_answer[n] / 6.0))
|
'Edge betweenness centrality: balanced tree'
| def test_balanced_tree(self):
| 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()):
assert_almost_equal(b[n], b_answer[n])
|
'Edge betweenness centrality: K5'
| def test_K5(self):
| G = nx.complete_graph(5)
b = nx.edge_betweenness_centrality(G, weight='weight', normalized=False)
b_answer = dict.fromkeys(G.edges(), 1)
for n in sorted(G.edges()):
assert_almost_equal(b[n], b_answer[n])
|
'Edge betweenness centrality: C4'
| def test_C4(self):
| 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()):
assert_almost_equal(b[n], b_answer[n])
|
'Edge betweenness centrality: P4'
| def test_P4(self):
| 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()):
assert_almost_equal(b[n], b_answer[n])
|
'Edge betweenness centrality: balanced tree'
| def test_balanced_tree(self):
| 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()):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: K4'
| def test_K4_normalized(self):
| G = nx.complete_graph(4)
b = nx.current_flow_betweenness_centrality(G, normalized=True)
b_answer = {0: 0.25, 1: 0.25, 2: 0.25, 3: 0.25}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
G.add_edge(0, 1, weight=0.5, other=0.3)
b = nx.current_flow_betweenness_centrality(G, normali... |
'Betweenness centrality: K4'
| def test_K4(self):
| G = nx.complete_graph(4)
for solver in ['full', 'lu', 'cg']:
b = nx.current_flow_betweenness_centrality(G, normalized=False, solver=solver)
b_answer = {0: 0.75, 1: 0.75, 2: 0.75, 3: 0.75}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: P4 normalized'
| def test_P4_normalized(self):
| G = nx.path_graph(4)
b = nx.current_flow_betweenness_centrality(G, normalized=True)
b_answer = {0: 0, 1: (2.0 / 3), 2: (2.0 / 3), 3: 0}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: P4'
| def test_P4(self):
| G = nx.path_graph(4)
b = nx.current_flow_betweenness_centrality(G, normalized=False)
b_answer = {0: 0, 1: 2, 2: 2, 3: 0}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: star'
| def test_star(self):
| G = nx.Graph()
nx.add_star(G, ['a', 'b', 'c', 'd'])
b = nx.current_flow_betweenness_centrality(G, normalized=True)
b_answer = {'a': 1.0, 'b': 0.0, 'c': 0.0, 'd': 0.0}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Betweenness centrality: alternate solvers'
| def test_solers(self):
| G = nx.complete_graph(4)
for solver in ['full', 'lu', 'cg']:
b = nx.current_flow_betweenness_centrality(G, normalized=False, solver=solver)
b_answer = {0: 0.75, 1: 0.75, 2: 0.75, 3: 0.75}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
|
'Approximate current-flow betweenness centrality: K4 normalized'
| def test_K4_normalized(self):
| G = nx.complete_graph(4)
b = nx.current_flow_betweenness_centrality(G, normalized=True)
epsilon = 0.1
ba = approximate_cfbc(G, normalized=True, epsilon=(0.5 * epsilon))
for n in sorted(G):
assert_allclose(b[n], ba[n], atol=epsilon)
|
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