def test_closeness_centrality(self):
c = bipartite.closeness_centrality(self.P4, [1, 3])
answer = {0: 2.0 / 3, 1: 1.0, 2: 1.0, 3: 2.0 / 3}
assert_equal(c, answer)
c = bipartite.closeness_centrality(self.K3, [0, 1, 2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0}
assert_equal(c, answer)
c = bipartite.closeness_centrality(self.C4, [0, 2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}
assert_equal(c, answer)
def test_closeness_centrality(self):
c = bipartite.closeness_centrality(self.P4, [1,3])
answer = {0: 2.0/3, 1: 1.0, 2: 1.0, 3:2.0/3}
assert_equal(c, answer)
c = bipartite.closeness_centrality(self.K3, [0,1,2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0}
assert_equal(c, answer)
c = bipartite.closeness_centrality(self.C4, [0,2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}
assert_equal(c, answer)
def test_closeness_centrality(self):
c = bipartite.closeness_centrality(self.P4, [1,3])
answer = {0: 2.0/3, 1: 1.0, 2: 1.0, 3:2.0/3}
assert_equal(c, answer)
c = bipartite.closeness_centrality(self.K3, [0,1,2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0}
assert_equal(c, answer)
c = bipartite.closeness_centrality(self.C4, [0,2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}
assert_equal(c, answer)
G = nx.Graph()
G.add_node(0)
G.add_node(1)
c = bipartite.closeness_centrality(G, [0])
assert_equal(c, {1: 0.0})
c = bipartite.closeness_centrality(G, [1])
assert_equal(c, {1: 0.0})
def test_closeness_centrality(self):
c = bipartite.closeness_centrality(self.P4, [1, 3])
answer = {0: 2.0 / 3, 1: 1.0, 2: 1.0, 3: 2.0 / 3}
assert c == answer
c = bipartite.closeness_centrality(self.K3, [0, 1, 2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0}
assert c == answer
c = bipartite.closeness_centrality(self.C4, [0, 2])
answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}
assert c == answer
G = nx.Graph()
G.add_node(0)
G.add_node(1)
c = bipartite.closeness_centrality(G, [0])
assert c == {1: 0.0}
c = bipartite.closeness_centrality(G, [1])
assert c == {1: 0.0}
def calculate_centrality(fp, centrality_type, perm_maps):
print '%s : start to read %s.txt '%(centrality_type, fp)
g = nx.Graph()
i_t = 100000
i_i = 0
p = 0
f = codecs.open('./txt_critical_perms/apps_file/%s.txt'%(fp), 'r', encoding='utf-8')
l = f.readline()
l = f.readline()
while l:
p, i_i = p_percent(p, i_i, i_t, 10)
ls = l.split('\t')
app_id = ls[0].strip().lower()
perm_id = ls[1].strip().lower()
g.add_node(app_id, bipartite=0) # top
g.add_node(perm_id, bipartite=1) # buttom
g.add_edge(app_id, perm_id)
l = f.readline()
is_connect = nx.is_connected(g)
print u'end read: %s'%(fp), is_connect
# buttom top
#node_data, node_app = bipartite.sets(g)
node_data = set(n for n, d in g.nodes(data=True) if d['bipartite'] == 1)
node_app = set(g) - node_data
## centrality degree
if centrality_type == 'degree':
try:
centrality = bipartite.degree_centrality(g, node_data)
result = get_centrality_out(fp, node_data, node_app, centrality, centrality_type, perm_maps)
return result, is_connect
except Exception as e:
print '** error in centrality: %s : %s'%(centrality_type, fp), e
## centrality closeness
if centrality_type == 'closeness':
try:
centrality = bipartite.closeness_centrality(g, node_app, normalized=False)
result = get_centrality_out(fp, node_data, node_app, centrality, centrality_type, perm_maps)
return result, is_connect
except Exception as e:
print '**** error in centrality : %s : %s'%(centrality_type, fp), e
## centrality betweenness
if centrality_type == 'betweenness':
try:
centrality = bipartite.betweenness_centrality(g, node_app)
result = get_centrality_out(fp, node_data, node_app, centrality, centrality_type, perm_maps)
return result, is_connect
except Exception as e:
print '**** error in centrality : %s : %s'%(centrality_type, fp), e
if centrality_type == 'clustering':
try:
centrality = bipartite.clustering(g, node_data, mode='dot')
result = get_centrality_out(fp, node_data, node_app, centrality, centrality_type, perm_maps)
return result, is_connect
except Exception as e:
print '**** error in centrality : %s : %s'%(centrality_type, fp), e
def info_network(G):
from networkx.algorithms import bipartite
from decimal import Decimal
print G.number_of_nodes()
print G.number_of_edges()
print "average_neighbor_degree"
dict = nx.average_neighbor_degree(G)
list1 = dict.keys()
list2 = dict.values()
print list1
print list2
print "degree_assortativity_coefficient"
print nx.degree_assortativity_coefficient(G)
print "degree_pearson_correlation_coefficient"
print nx.degree_pearson_correlation_coefficient(G)
# print nx.k_nearest_neighbors(G)
print "STOP HERE"
print "bipartite.closeness_centrality(G,G.node)"
dict2 = bipartite.closeness_centrality(G, G.node)
list3 = dict2.values()
print list3
print "nx.degree_centrality(G)"
dict3 = nx.degree_centrality(G)
list4 = dict3.values()
print list4
print "nx.betweenness_centrality(G)"
dict4 = nx.betweenness_centrality(G)
list5 = dict4.values()
print list5
print "hits_numpy"
dict5 = nx.hits_numpy(G)
print dict5
def test_davis_closeness_centrality(self):
G = self.davis
clos = bipartite.closeness_centrality(G, self.top_nodes)
answer = {
"E8": 0.85,
"E9": 0.79,
"E7": 0.73,
"Nora Fayette": 0.80,
"Evelyn Jefferson": 0.80,
"Theresa Anderson": 0.80,
"E6": 0.69,
"Sylvia Avondale": 0.77,
"Laura Mandeville": 0.73,
"Brenda Rogers": 0.73,
"Katherina Rogers": 0.73,
"E5": 0.59,
"Helen Lloyd": 0.73,
"E3": 0.56,
"Ruth DeSand": 0.71,
"Verne Sanderson": 0.71,
"E12": 0.56,
"Myra Liddel": 0.69,
"E11": 0.54,
"Eleanor Nye": 0.67,
"Frances Anderson": 0.67,
"Pearl Oglethorpe": 0.67,
"E4": 0.54,
"Charlotte McDowd": 0.60,
"E10": 0.55,
"Olivia Carleton": 0.59,
"Flora Price": 0.59,
"E2": 0.52,
"E1": 0.52,
"Dorothy Murchison": 0.65,
"E13": 0.52,
"E14": 0.52,
}
for node, value in answer.items():
assert almost_equal(value, clos[node], places=2)
def test_davis_closeness_centrality(self):
G = self.davis
clos = bipartite.closeness_centrality(G, self.top_nodes)
answer = {
'E8': 0.85,
'E9': 0.79,
'E7': 0.73,
'Nora Fayette': 0.80,
'Evelyn Jefferson': 0.80,
'Theresa Anderson': 0.80,
'E6': 0.69,
'Sylvia Avondale': 0.77,
'Laura Mandeville': 0.73,
'Brenda Rogers': 0.73,
'Katherina Rogers': 0.73,
'E5': 0.59,
'Helen Lloyd': 0.73,
'E3': 0.56,
'Ruth DeSand': 0.71,
'Verne Sanderson': 0.71,
'E12': 0.56,
'Myra Liddel': 0.69,
'E11': 0.54,
'Eleanor Nye': 0.67,
'Frances Anderson': 0.67,
'Pearl Oglethorpe': 0.67,
'E4': 0.54,
'Charlotte McDowd': 0.60,
'E10': 0.55,
'Olivia Carleton': 0.59,
'Flora Price': 0.59,
'E2': 0.52,
'E1': 0.52,
'Dorothy Murchison': 0.65,
'E13': 0.52,
'E14': 0.52
}
for node, value in answer.items():
assert almost_equal(value, clos[node], places=2)
def compute_centrality(nets=None, names=None):
datet = datetime.datetime.today()
date = datet.strftime("%Y%m%d%H%M")
if names is None:
names = default_years
if nets is None:
nets = networks_by_year()
result = {}
for name, G in zip(names, nets):
result[name] = {}
print("computing centrality for {}".format(name))
devs = set(n for n, d in G.nodes(data=True) if d['bipartite']==1)
result[name]['deg'] = bp.degree_centrality(G, devs)
try:
result[name]['bet'] = bp.betweenness_centrality(G, devs)
except ZeroDivisionError:
result[name]['bet'] = dict()
result[name]['clos'] = bp.closeness_centrality(G, devs)
result[name]['ev'] = nx.eigenvector_centrality_numpy(G)
fn = 'years' if name == 2014 else 'branches'
fname = "{0}/bipartite_centrality_{1}_{2}.pkl".format(results_dir, fn, date)
utils.write_results_pkl(result, fname)
def test_davis_closeness_centrality(self):
G = self.davis
clos = bipartite.closeness_centrality(G, self.top_nodes)
answer = {'E8':0.85,
'E9':0.79,
'E7':0.73,
'Nora Fayette':0.80,
'Evelyn Jefferson':0.80,
'Theresa Anderson':0.80,
'E6':0.69,
'Sylvia Avondale':0.77,
'Laura Mandeville':0.73,
'Brenda Rogers':0.73,
'Katherina Rogers':0.73,
'E5':0.59,
'Helen Lloyd':0.73,
'E3':0.56,
'Ruth DeSand':0.71,
'Verne Sanderson':0.71,
'E12':0.56,
'Myra Liddel':0.69,
'E11':0.54,
'Eleanor Nye':0.67,
'Frances Anderson':0.67,
'Pearl Oglethorpe':0.67,
'E4':0.54,
'Charlotte McDowd':0.60,
'E10':0.55,
'Olivia Carleton':0.59,
'Flora Price':0.59,
'E2':0.52,
'E1':0.52,
'Dorothy Murchison':0.65,
'E13':0.52,
'E14':0.52}
for node, value in answer.items():
assert_almost_equal(value, clos[node], places=2)
G.add_edges_from([(row[0],row[1])],weight=row[2]) print "average_neighbor_degree" print nx.average_neighbor_degree(G) print "degree_assortativity_coefficient" print nx.degree_assortativity_coefficient(G) print "degree_pearson_correlation_coefficient" print nx.degree_pearson_correlation_coefficient(G) #print nx.k_nearest_neighbors(G) print "bipartite.closeness_centrality" print bipartite.closeness_centrality(G,G.node) print "degree_centrality" print nx.degree_centrality(G) print "betweenness_centrality" print nx.betweenness_centrality(G) print "k_nearest_neighbors" print nx.k_nearest_neighbors(G) #print nx.current_flow_closeness_centrality(G, normalized=True, weight='weight', dtype='float', solver='lu') #centrality=nx.eigenvector_centrality(G) #print(['%s %0.2f'%(node,centrality[node]) for node in centrality]) #print nx.eigenvector_centrality(G, max_iter=100, tol=1e-02, nstart=None)
def closeness_centrality(self):
self.closeness_centrality_dict = bi.closeness_centrality(self.G, self.nodes)
print('%d %s' % (W2.degree(c, weight='weight'), c))
print
nx.draw(W2, node_color='b', edge_color='r', with_labels=True)
plt.savefig("davisontoclubsratio.png") # save as png
plt.show() # display
print
print
# Degee Summary Stats
deg = bipartite.degree_centrality(g, clubs)
# Betweenness Summary Stats
bc = bipartite.betweenness_centrality(g, clubs)
# Closeness Summary Stats
cc = bipartite.closeness_centrality(g, clubs)
maxdeg = 0
mindeg = 9999
mindegwomen = []
maxdegwomen = []
degarray = []
maxbc = 0
minbc = 9999
minbcwomen = []
maxbcwomen = []
bcarray = []
maxcc = 0
mincc = 9999
# ADD PLOTS OF DEGREE DISTRIBUTION CONSIDERING EACH EDGE TYPE (3 PLOTS)
# Connectedness
numSCC = nx.number_strongly_connected_components(network)
numWCC = nx.number_weakly_connected_components(network)
# Clustering
# No C3 clustering by definition of bipartite, elaborate and explain C4 during talk
cluster1 = nx.square_clustering(
network) # No clustering because edges only go from users to designs
cluster2 = bipartite.clustering(
network) # No clustering because edges only go from users to designs
# Centrality Measures
# Do these factor in directedness!!!!!!!!!!!!!!!!!!!!!!!!!???????????????????????
closeness_centrality = bipartite.closeness_centrality(network, users)
total_closeness_centrality = 0
for key, value in closeness_centrality.items():
total_closeness_centrality += value
avg_closeness_centrality = total_closeness_centrality / len(
closeness_centrality)
degree_centrality = bipartite.degree_centrality(network, users)
total_degree_centrality = 0
for key, value in degree_centrality.items():
total_degree_centrality += value
avg_degree_centrality = total_degree_centrality / len(degree_centrality)
betweenness_centrality = bipartite.betweenness_centrality(network, users)
total_betweenness_centrality = 0
for key, value in betweenness_centrality.items():
dict = nx.average_neighbor_degree(G) list1 = dict.keys() list2 = dict.values() print list1 print list2 print "degree_assortativity_coefficient" print nx.degree_assortativity_coefficient(G) print "degree_pearson_correlation_coefficient" print nx.degree_pearson_correlation_coefficient(G) #print nx.k_nearest_neighbors(G) print "STOP HERE" print "bipartite.closeness_centrality(G,G.node)" dict2 = bipartite.closeness_centrality(G,G.node) list3 = dict2.values() print list3 print "nx.degree_centrality(G)" dict3 = nx.degree_centrality(G) list4 = dict3.values() print list4 print "nx.betweenness_centrality(G)" dict4 = nx.betweenness_centrality(G) list5 = dict4.values() print list5 #print nx.current_flow_closeness_centrality(G, normalized=True, weight='weight', dtype='float', solver='lu') #centrality=nx.eigenvector_centrality(G)
