def test_no_weight(self):
inf = float('inf')
expected = {(3, 4, inf), (4, 3, inf)}
assert_in(next(nx.local_bridges(self.BB)), expected)
expected = {(u, v, 3) for u, v, in self.square.edges}
assert_equal(set(nx.local_bridges(self.square)), expected)
assert_equal(list(nx.local_bridges(self.tri)), [])
def test_no_weight(self):
inf = float('inf')
expected = {(3, 4, inf), (4, 3, inf)}
assert next(nx.local_bridges(self.BB)) in expected
expected = {(u, v, 3) for u, v, in self.square.edges}
assert set(nx.local_bridges(self.square)) == expected
assert list(nx.local_bridges(self.tri)) == []
def test_no_weight(self):
inf = float('inf')
expected = {(3, 4, inf), (4, 3, inf)}
assert_in(next(nx.local_bridges(self.BB)), expected)
expected = {(u, v, 3) for u, v, in self.square.edges}
assert_equal(set(nx.local_bridges(self.square)), expected)
assert_equal(list(nx.local_bridges(self.tri)), [])
def test_weight(self):
inf = float('inf')
G = self.square.copy()
G.edges[1, 2]['weight'] = 2
expected = {(u, v, 5 - wt) for u, v, wt in G.edges(data='weight', default=1)}
assert_equal(set(nx.local_bridges(G, weight='weight')), expected)
expected = {(u, v, 6) for u, v in G.edges}
lb = nx.local_bridges(G, weight=lambda u, v, d: 2)
assert_equal(set(lb), expected)
def test_weight(self):
inf = float("inf")
G = self.square.copy()
G.edges[1, 2]["weight"] = 2
expected = {(u, v, 5 - wt)
for u, v, wt in G.edges(data="weight", default=1)}
assert set(nx.local_bridges(G, weight="weight")) == expected
expected = {(u, v, 6) for u, v in G.edges}
lb = nx.local_bridges(G, weight=lambda u, v, d: 2)
assert set(lb) == expected
def test_nospan(self):
expected = {(3, 4), (4, 3)}
assert_in(next(nx.local_bridges(self.BB, with_span=False)), expected)
assert_equal(set(nx.local_bridges(self.square, with_span=False)),
self.square.edges)
assert_equal(list(nx.local_bridges(self.tri, with_span=False)), [])
def test_nospan(self):
expected = {(3, 4), (4, 3)}
assert_in(next(nx.local_bridges(self.BB, with_span=False)), expected)
assert_equal(set(nx.local_bridges(self.square, with_span=False)), self.square.edges)
assert_equal(list(nx.local_bridges(self.tri, with_span=False)), [])
def test_nospan(self):
expected = {(3, 4), (4, 3)}
assert next(nx.local_bridges(self.BB, with_span=False)) in expected
assert set(nx.local_bridges(self.square,
with_span=False)) == self.square.edges
assert list(nx.local_bridges(self.tri, with_span=False)) == []
# https://stackoverflow.com/questions/43208737/using-networkx-to-calculate-eigenvector-centrality?rq=1
lEigenVectorCentrality = nx.eigenvector_centrality_numpy(network_weight)
for nodeId, cent in lEigenVectorCentrality.items():
network_weight.node[nodeId]['eigen'] = float(cent)
# katz centrality
lKatzCentrality = nx.katz_centrality(network_weight)
for nodeId, cent in lKatzCentrality.items():
network_weight.node[nodeId]['katz'] = float(cent)
# Betweeness centrality
lBetCentrality = nx.betweenness_centrality(network_weight)
for nodeId, cent in lBetCentrality.items():
network_weight.node[nodeId]['betweenness'] = float(cent)
# %%
for g in nx.local_bridges(network_weight, with_span=True, weight=None):
if g[2] < 100:
print(g)
# %%
import altair as alt
alt.renderers.enable('notebook')
# %%
# Generating Data
source = pd.DataFrame({
'Degree Centrality': list(lDegCentrality.values()),
# 'Eigenvector Centrality': list(lEigenVectorCentrality.values()),
'Betweenness Centrality': list(lBetCentrality.values()),
'Katz Centrality': list(lKatzCentrality.values())
})
bridge_count = []
local_bridge_count = []
c1, c2 = 0, 0
#C between a bully and a victim in Gs is a bridge or a local bridge
for graph in Gs:
graph1 = graph.to_undirected()
#Get all the bridges
bridges = list(nx.bridges(graph1))
#Check if a bully and a victim in Gs is a bridge or a local bridge
for bridge in bridges:
if IsBridgeBetweenBullyAndVictim(graph1, bridge):
c1 += 1
bridge_count.append(c1)
c1 = 0
#Get all the local bridges
local_bridges = list(nx.local_bridges(graph1))
for local_bridge in local_bridges:
if IsBridgeBetweenBullyAndVictim(graph1, local_bridge):
c2 += 1
local_bridge_count.append(c2)
c2 = 0
#print(sum(bridge_count))
#print(sum(local_bridge_count))
#e Compute the principal eigenvalue, λws, of the weighted and undirected adjacency matrix of each Gs
print('Number of nodes of each graph: \n', num_of_nodes)
print('Number of edges of each graph: \n', num_of_nodes)
print('Total degree of each graph: \n', total_degrees)
#Convert graphs to undirected to calculate principal eigen value
Hs = []
