def test_p4_edge_load(self):
G=self.P4
c = nx.edge_load_centrality(G)
d={(0, 1): 6.000,
(1, 2): 8.000,
(2, 3): 6.000}
for n in G.edges():
assert_almost_equal(c[n],d[n],places=3)
def test_p4_edge_load(self):
G = self.P4
c = nx.edge_load_centrality(G)
d = {(0, 1): 6.000,
(1, 2): 8.000,
(2, 3): 6.000}
for n in G.edges():
assert almost_equal(c[n], d[n], places=3)
def test_tree_edge_load(self):
G=self.T
c = nx.edge_load_centrality(G)
d={(0, 1): 24.000,
(0, 2): 24.000,
(1, 3): 12.000,
(1, 4): 12.000,
(2, 5): 12.000,
(2, 6): 12.000}
for n in G.edges():
assert_almost_equal(c[n],d[n],places=3)
def test_tree_edge_load(self):
G = self.T
c = nx.edge_load_centrality(G)
d = {(0, 1): 24.000,
(0, 2): 24.000,
(1, 3): 12.000,
(1, 4): 12.000,
(2, 5): 12.000,
(2, 6): 12.000}
for n in G.edges():
assert almost_equal(c[n], d[n], places=3)
def test_tree_edge_load(self):
G = self.T
c = nx.edge_load_centrality(G)
d = {
(0, 1): 24.000,
(0, 2): 24.000,
(1, 3): 12.000,
(1, 4): 12.000,
(2, 5): 12.000,
(2, 6): 12.000,
}
for n in G.edges():
assert c[n] == pytest.approx(d[n], abs=1e-3)
def test_k5_edge_load(self):
G=self.K5
c = nx.edge_load_centrality(G)
d={(0, 1): 5.000,
(0, 2): 5.000,
(0, 3): 5.000,
(0, 4): 5.000,
(1, 2): 5.000,
(1, 3): 5.000,
(1, 4): 5.000,
(2, 3): 5.000,
(2, 4): 5.000,
(3, 4): 5.000}
for n in G.edges():
assert_almost_equal(c[n],d[n],places=3)
def test_k5_edge_load(self):
G = self.K5
c = nx.edge_load_centrality(G)
d = {(0, 1): 5.000,
(0, 2): 5.000,
(0, 3): 5.000,
(0, 4): 5.000,
(1, 2): 5.000,
(1, 3): 5.000,
(1, 4): 5.000,
(2, 3): 5.000,
(2, 4): 5.000,
(3, 4): 5.000}
for n in G.edges():
assert almost_equal(c[n], d[n], places=3)
def test_k5_edge_load(self):
G = self.K5
c = nx.edge_load_centrality(G)
d = {
(0, 1): 5.000,
(0, 2): 5.000,
(0, 3): 5.000,
(0, 4): 5.000,
(1, 2): 5.000,
(1, 3): 5.000,
(1, 4): 5.000,
(2, 3): 5.000,
(2, 4): 5.000,
(3, 4): 5.000,
}
for n in G.edges():
assert c[n] == pytest.approx(d[n], abs=1e-3)
def get_central_edges(g, allowed, n=20, method="load_centrality"):
nx_g = pyzx2nx(g.copy())
if method == "load_centrality":
centralities = nx.edge_load_centrality(nx_g)
elif method == "betweenness_centrality":
centralities = nx.edge_betweenness_centrality(nx_g)
elif method == "pos_dispersion":
centralities = {(s, t): nx.dispersion(nx_g, u=s, v=t)
for (s, t) in g.edges()}
elif method == "neg_dispersion":
centralities = {(s, t): -nx.dispersion(nx_g, u=s, v=t)
for (s, t) in g.edges()}
elif method == "degree":
dgs = nx_g.degree()
centralities = {(s, t): dgs[s] + dgs[t] for (s, t) in g.edges()}
elif method == "sum_neighb_degree":
dgs = nx_g.degree()
all_neighbors = {
(s, t):
list(set(list(nx_g.neighbors(s)) + list(nx_g.neighbors(t))))
for (s, t) in g.edges()
}
centralities = {(s, t): sum([dgs[n] for n in ns])
for ((s, t), ns) in all_neighbors.items()}
elif method == "avg_neighb_degree":
dgs = nx_g.degree()
all_neighbors = {
(s, t):
list(set(list(nx_g.neighbors(s)) + list(nx_g.neighbors(t))))
for (s, t) in g.edges()
}
centralities = {(s, t): np.mean([dgs[n] for n in ns])
for ((s, t), ns) in all_neighbors.items()}
else:
raise RuntimeError(f"[get_central_edges] Unrecognized method {method}")
allowed_centralities = {e: centralities[e] for e in allowed}
sum_allowed = sum(allowed_centralities.values())
weights = [float(v) / sum_allowed for v in allowed_centralities.values()]
# ranked_edges = sorted(allowed_centralities.items(), key=lambda item: -item[1])
# top_n = [k for (k, _) in ranked_edges[:n]]
# return top_n
return weights
def test_p4_edge_load(self):
G = self.P4
c = nx.edge_load_centrality(G)
d = {(0, 1): 6.000, (1, 2): 8.000, (2, 3): 6.000}
for n in G.edges():
assert c[n] == pytest.approx(d[n], abs=1e-3)
def set_edge_weight(self, edge_weight_method='weight'):
if edge_weight_method == 'weight':
return
# Centrality based methods
elif edge_weight_method == 'edge_betweenness_centrality':
print("comptuing edge_betweenness_centrality..")
C = nx.edge_betweenness_centrality(self.G, weight='weight')
print("done!")
elif edge_weight_method == 'edge_betweenness_centrality_subset':
print("comptuing edge_betweenness_centrality_subset..")
C = nx.edge_current_flow_betweenness_centrality(self.G,
weight='weight')
print('done')
elif edge_weight_method == 'edge_current_flow_betweenness_centrality_subset':
print(
"comptuing edge_current_flow_betweenness_centrality_subset..")
C = nx.edge_current_flow_betweenness_centrality_subset(
self.G, weight='weight')
print('done')
elif edge_weight_method == 'edge_load_centrality':
print("comptuing edge_load_centrality..")
C = nx.edge_load_centrality(self.G)
print('done!')
# Link Prediction based methods
elif edge_weight_method == 'adamic_adar_index':
print("comptuing adamic_adar_index ..")
preds = nx.adamic_adar_index(self.G, self.G.edges())
C = {}
for u, v, p in preds:
C[(u, v)] = p
elif edge_weight_method == 'ra_index_soundarajan_hopcroft':
print("comptuing ra_index_soundarajan_hopcroft ..")
preds = nx.ra_index_soundarajan_hopcroft(self.G, self.G.edges())
C = {}
for u, v, p in preds:
C[(u, v)] = p
elif edge_weight_method == 'preferential_attachment':
print("comptuing preferential_attachment ..")
preds = nx.preferential_attachment(self.G, self.G.edges())
C = {}
for u, v, p in preds:
C[(u, v)] = p
#elif edge_weight_method=='cn_soundarajan_hopcroft':
# print("comptuing cn_soundarajan_hopcroft ..")
# preds=nx.cn_soundarajan_hopcroft(self.G,self.G.edges())
# C={}
# for u, v, p in preds:
# C[(u,v)]=p
elif edge_weight_method == 'within_inter_cluster':
print("comptuing within_inter_cluster ..")
preds = nx.within_inter_cluster(self.G, self.G.edges())
C = {}
for u, v, p in preds:
C[(u, v)] = p
elif edge_weight_method == 'resource_allocation_index':
print("comptuing resource allocation index ..")
preds = nx.resource_allocation_index(self.G, self.G.edges())
C = {}
for u, v, p in preds:
C[(u, v)] = p
elif edge_weight_method == 'jaccard_coefficient':
print("comptuing jaccard_coefficient..")
preds = nx.jaccard_coefficient(self.G, self.G.edges())
C = {}
for u, v, p in preds:
C[(u, v)] = p
print('done!')
for u, v, d in self.G.edges(data=True):
if edge_weight_method == None:
d['weight'] = 1
else:
d['weight'] = C[(u, v)]
return 1
for i in range(n):
if (str(int(t_val[i,0])),str(int(t_val[i,1]))) in edge_cu.keys():
t_val[i,6]=edge_cu[(str(int(t_val[i,0])),str(int(t_val[i,1])))]
if (str(int(t_val[i,1])),str(int(t_val[i,0]))) in edge_cu.keys():
t_val[i,6]=edge_cu[(str(int(t_val[i,1])),str(int(t_val[i,0])))]
sp_cu=stats.spearmanr(t_val[:,2],t_val[:,6])
print("edge_current_flow_betweenness_centrality",sp_cu)
# In[100]:
#########task 7 - edge_load_centrality
edge_lo=nx.edge_load_centrality(net)
for i in range(n):
if (str(int(t_val[i,0])),str(int(t_val[i,1]))) in edge_lo.keys():
t_val[i,7]=edge_lo[(str(int(t_val[i,0])),str(int(t_val[i,1])))]
if (str(int(t_val[i,1])),str(int(t_val[i,0]))) in edge_lo.keys():
t_val[i,7]=edge_lo[(str(int(t_val[i,1])),str(int(t_val[i,0])))]
sp_lo=stats.spearmanr(t_val[:,2],t_val[:,7])
print("edge_load_centrality",sp_lo)
# In[116]:
#########task 7 - thresholded networks
edges = net.edges.data()
def edge_load_centrality(G, scale):
return remove_transition(G, nx.edge_load_centrality(G), scale)
