def edge_centrality(net):
values ={}
bet = nx.edge_betweenness(net,normalized= True)
flow = nx.edge_current_flow_betweenness_centrality(net,normalized= True)
load = nx.edge_load(net)
com = nx.communicability(net)
bet_list =[]
flow_list = []
load_list = []
com_list = []
for edge,value in bet.iteritems() :
origin,end = edge
value_flow = max(flow.get(edge),flow.get((end,origin)))
values[edge] = [value,value_flow,load.get(edge),com.get(origin).get(end)]
bet_list.append(value)
flow_list.append(value_flow)
load_list.append(load.get(edge))
com_list.append(com.get(origin).get(end))
file3 = open("bl.csv",'w')
for xt in [bet_list,load_list,flow_list,com_list] :
for yt in [bet_list,load_list,flow_list,com_list] :
corr(xt,yt,file3)
print
file3.write("\n")
file3.close()
return values
def edge_centrality(net):
values = {}
bet = nx.edge_betweenness(net, normalized=True)
flow = nx.edge_current_flow_betweenness_centrality(net, normalized=True)
load = nx.edge_load(net)
com = nx.communicability(net)
bet_list = []
flow_list = []
load_list = []
com_list = []
for edge, value in bet.iteritems():
origin, end = edge
value_flow = max(flow.get(edge), flow.get((end, origin)))
values[edge] = [
value, value_flow,
load.get(edge),
com.get(origin).get(end)
]
bet_list.append(value)
flow_list.append(value_flow)
load_list.append(load.get(edge))
com_list.append(com.get(origin).get(end))
file3 = open("bl.csv", 'w')
for xt in [bet_list, load_list, flow_list, com_list]:
for yt in [bet_list, load_list, flow_list, com_list]:
corr(xt, yt, file3)
print
file3.write("\n")
file3.close()
return values
def test_p4_edge_load(self):
G=self.P4
c = nx.edge_load(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(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(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(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_k5_edge_load(self):
G=self.K5
c = nx.edge_load(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(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 edge_attributes(self):
result = {}
result['edge_betweenness_centrality'] = nx.edge_betweenness_centrality(
self.graph)
result['edge_load'] = nx.edge_load(self.graph)
edge_attributes = []
for edge in self.graph.edges():
edge_attributes.append(
(edge, result['edge_betweenness_centrality'][edge],
result['edge_load'][edge]))
edge_attributes.insert(
0, ['edge', 'edge_betweenness_centrality', 'edge_load'])
return edge_attributes
def edgeCentralities(df, G):
DF2 = pd.DataFrame(nx.edge_betweenness_centrality(G),
index=['edge_betweenness_centrality'])
DF2 = DF2.T.unstack(level=0)
DF2.columns = DF2.columns.get_level_values(1)
DF3 = pd.DataFrame(nx.edge_current_flow_betweenness_centrality(G,normalized=True),
index=['edge_current_flow_betweenness_centrality'])
DF3 = DF3.T.unstack(level=0)
DF3.columns = DF3.columns.get_level_values(1)
DF4 = pd.DataFrame(nx.edge_load(G),
index=['edge_load'])
DF4 = DF4.T.unstack(level=0)
DF4.columns = DF4.columns.get_level_values(1)
DFB = pd.concat([DF2, DF3, DF4], axis=0) #buggy
return pd.Panel({'edge_betweenness_centrality':DF2,
'edge_current_flow_betweenness_centrality':DF3,
'edge_load':DF4})
def centrality(self):
result = {}
result['degree_centrality'] = nx.degree_centrality(self.graph)
if self.directed == 'directed':
result['in_degree_centrality'] = nx.in_degree_centrality(
self.graph)
result['out_degree_centrality'] = nx.out_degree_centrality(
self.graph)
result['closeness_centrality'] = nx.closeness_centrality(self.graph)
result['betweenness_centrality'] = nx.betweenness_centrality(
self.graph)
# fix the tuple cant decode into json problem
stringify_temp = {}
temp = nx.edge_betweenness_centrality(self.graph)
for key in temp.keys():
stringify_temp[str(key)] = temp[key]
result['edge_betweenness_centrality'] = stringify_temp
if self.directed == 'undirected':
result[
'current_flow_closeness_centrality'] = nx.current_flow_closeness_centrality(
self.graph)
result[
'current_flow_betweenness_centrality'] = nx.current_flow_betweenness_centrality(
self.graph)
stringify_temp = {}
temp = nx.edge_current_flow_betweenness_centrality(self.graph)
for key in temp.keys():
stringify_temp[str(key)] = temp[key]
result['edge_current_flow_betweenness_centrality'] = stringify_temp
result[
'approximate_current_flow_betweenness_centrality'] = nx.approximate_current_flow_betweenness_centrality(
self.graph)
result['eigenvector_centrality'] = nx.eigenvector_centrality(
self.graph)
result[
'eigenvector_centrality_numpy'] = nx.eigenvector_centrality_numpy(
self.graph)
result['katz_centrality'] = nx.katz_centrality(self.graph)
result['katz_centrality_numpy'] = nx.katz_centrality_numpy(
self.graph)
result['communicability'] = nx.communicability(self.graph)
result['communicability_exp'] = nx.communicability_exp(self.graph)
result[
'communicability_centrality'] = nx.communicability_centrality(
self.graph)
result[
'communicability_centrality_exp'] = nx.communicability_centrality_exp(
self.graph)
result[
'communicability_betweenness_centrality'] = nx.communicability_betweenness_centrality(
self.graph)
result['estrada_index'] = nx.estrada_index(self.graph)
result['load_centrality'] = nx.load_centrality(self.graph)
stringify_temp = {}
temp = nx.edge_load(self.graph)
for key in temp.keys():
stringify_temp[str(key)] = temp[key]
result['edge_load'] = stringify_temp
result['dispersion'] = nx.dispersion(self.graph)
fname_centra = self.DIR + '/centrality.json'
with open(fname_centra, "w") as f:
json.dump(result, f, cls=SetEncoder, indent=2)
print(fname_centra)
def edgeload(net):
return distri(nx.edge_load(net).values(),'edgeload')
def edgeload(net):
return distri(nx.edge_load(net).values(), 'edgeload')
def add_load_edge(graf):
print "Adding load to edges"
l_dict = nx.edge_load(graf)
nx.set_edge_attributes(graf, 'loa', l_dict)
import networkx as nx
import json
import matplotlib.cm as cm
import matplotlib.colors as colors
from networkx.readwrite import json_graph
G = nx.barabasi_albert_graph(250,1)
#G = nx.random_geometric_graph(256,.1)
cc = nx.closeness_centrality(G)
d = G.degree()
nd = nx.average_neighbor_degree(G)
eb = nx.edge_betweenness(G)
el = nx.edge_load(G)
min_cc = min(cc.values())
max_cc = max(cc.values())
for n in cc:
cc[n] = (cc[n]-min_cc)/(max_cc-min_cc)
min_eb = min(eb.values())
max_eb = max(eb.values())
for e in eb:
eb[e] = (eb[e]-min_eb)/(max_eb-min_eb)
min_el = min(el.values())
max_el = max(el.values())
def add_load_edge(graf):
print "Adding load to edges"
l_dict = nx.edge_load(graf)
nx.set_edge_attributes(graf, 'loa', l_dict)
