def test_K5(self):
"""Edge betweenness centrality: K5"""
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])
def most_important_nodes_edges_subset(self, graph, source_nodes, target_nodes, T=0, normalized=False, directed=False):
cv=check_graph_validity.Graphs()
ret = cv.is_valid_most_important_graph(graph, source_nodes, target_nodes,T)
if(not ret[0]):
ret.append({})
print (ret)
return ret
try:
# construct networkx graph from given graph
if (directed):
G=nx.DiGraph()
else:
G = nx.Graph()
G.add_nodes_from(graph['nodes'])
G.add_edges_from(graph['edges'])
if 'weights' in graph:
for i in range(len(graph['edges'])):
G[graph['edges'][i][0]][graph['edges'][i][1]]['weight'] = graph['weights'][i]
except Exception as e:
return ["False", str(e),{}]
output={}
# clearDict={}
result = None
if (T == 0):
result=nx.betweenness_centrality_subset(G, source_nodes, target_nodes, normalized, weight='weights')
#remove nodes that are either in source_node or in target_node
# for key,val in result.items():
# if (not(key in source_nodes or key in target_nodes)):
# clearDict[key]=val
# print(clearDict)
elif (T == 1):
result =nx.edge_betweenness_centrality_subset(G, source_nodes, target_nodes, normalized, weight='weights')
# for key,val in result.items():
# #remove nodes that are either in source_node or in target_node
# if(not(key[0] in source_nodes or key[0] in target_nodes or key[1] in source_nodes or key[1] in target_nodes)):
# clearDict[key]=val
# print(clearDict)
# output["betweenness_centrality"] = list(max(clearDict.items(), key=lambda k: k[1]))
highest = max(result.values())
nodes_list = [k for k, v in result.items() if v == highest]
output["betweenness_centrality"] = [nodes_list,highest] #result
print (output)
return [True, 'success', output]
def test_P5(self):
"""Edge betweenness centrality: P5"""
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])
def test_box(self):
"""Edge etweenness centrality: box"""
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.edges()):
assert_almost_equal(b[n], b_answer[n])
def test_P5_directed(self):
"""Edge betweenness centrality: P5 directed"""
G = networkx.DiGraph()
G.add_path(range(5))
b_answer = dict.fromkeys(G.edges(), 0)
b_answer[(0, 1)] = 1
b_answer[(1, 2)] = 1
b_answer[(2, 3)] = 1
b = edge_betweenness_centrality_subset(G, sources=[0], targets=[3], weighted_edges=False)
for n in sorted(G.edges()):
assert_almost_equal(b[n], b_answer[n])
def test_P5_directed(self):
"""Edge betweenness subset centrality: P5 directed"""
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 b[n] == pytest.approx(b_answer[n], abs=1e-7)
def test_K5(self):
"""Edge betweenness centrality: K5"""
G = networkx.complete_graph(5)
b = edge_betweenness_centrality_subset(G,
sources=[0],
targets=[1, 3],
weighted_edges=False)
b_answer = dict.fromkeys(G.edges(), 0)
b_answer[(0, 3)] = 0.5
b_answer[(0, 1)] = 0.5
for n in sorted(G.edges()):
print(n, b[n])
assert_almost_equal(b[n], b_answer[n])
def test_box_and_path2(self):
"""Edge betweenness centrality: box and path multiple target"""
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_centrality_subset(G, sources=[0],
targets=[3, 4], weight=None)
for n in sorted(G.edges()):
assert_almost_equal(b[n], b_answer[n])
def test_P5(self):
"""Edge betweenness centrality: P5"""
G = networkx.Graph()
G.add_path(list(range(5)))
b_answer = dict.fromkeys(G.edges(), 0)
b_answer[(0, 1)] = 0.5
b_answer[(1, 2)] = 0.5
b_answer[(2, 3)] = 0.5
b = edge_betweenness_centrality_subset(G,
sources=[0],
targets=[3],
weighted_edges=False)
for n in sorted(G.edges()):
assert_almost_equal(b[n], b_answer[n])
def test_box_and_path(self):
"""Edge betweenness subset centrality: box and path"""
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],
targets=[3, 4],
weight=None)
for n in sorted(G.edges()):
assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
def test_P5_multiple_target(self):
"""Edge betweenness centrality: P5 multiple target"""
G=networkx.Graph()
G.add_path(list(range(5)))
b_answer=dict.fromkeys(G.edges(),0)
b_answer[(0,1)]=1
b_answer[(1,2)]=1
b_answer[(2,3)]=1
b_answer[(3,4)]=0.5
b=edge_betweenness_centrality_subset(G,
sources=[0],
targets=[3,4],
weight=None)
for n in sorted(G.edges()):
assert_almost_equal(b[n],b_answer[n])
def test_box_and_path(self):
"""Edge etweenness centrality: box and path"""
G = networkx.Graph()
G.add_edge(0, 1)
G.add_edge(0, 2)
G.add_edge(1, 3)
G.add_edge(2, 3)
G.add_edge(3, 4)
G.add_edge(4, 5)
b_answer = dict.fromkeys(G.edges(), 0)
b_answer[(0, 1)] = 1.0 / 2
b_answer[(0, 2)] = 1.0 / 2
b_answer[(1, 3)] = 1.0 / 2
b_answer[(2, 3)] = 1.0 / 2
b_answer[(3, 4)] = 1.0 / 2
b = edge_betweenness_centrality_subset(G, sources=[0], targets=[3, 4], weighted_edges=False)
for n in sorted(G.edges()):
assert_almost_equal(b[n], b_answer[n])
def test_box(self):
"""Edge etweenness centrality: box"""
G = networkx.Graph()
G.add_edge(0, 1)
G.add_edge(0, 2)
G.add_edge(1, 3)
G.add_edge(2, 3)
b_answer = dict.fromkeys(G.edges(), 0)
b_answer[(0, 1)] = 0.25
b_answer[(0, 2)] = 0.25
b_answer[(1, 3)] = 0.25
b_answer[(2, 3)] = 0.25
b = edge_betweenness_centrality_subset(G,
sources=[0],
targets=[3],
weighted_edges=False)
for n in sorted(G.edges()):
assert_almost_equal(b[n], b_answer[n])
def test_box_and_path(self):
"""Edge etweenness centrality: box and path"""
G = nx.Graph()
G.add_edge(0, 1)
G.add_edge(0, 2)
G.add_edge(1, 3)
G.add_edge(2, 3)
G.add_edge(3, 4)
G.add_edge(4, 5)
b_answer = dict.fromkeys(G.edges(), 0)
b_answer[(0, 1)] = 1.0 / 2
b_answer[(0, 2)] = 1.0 / 2
b_answer[(1, 3)] = 1.0 / 2
b_answer[(2, 3)] = 1.0 / 2
b_answer[(3, 4)] = 1.0 / 2
b = edge_betweenness_centrality_subset(G,
sources=[0],
targets=[3, 4],
weight=None)
for n in sorted(G.edges()):
assert_almost_equal(b[n], b_answer[n])
