def betweenComDetect(G1):
G = G1.copy()
curEdge = nx.edge_betweenness(G)
res = []
mod1 = -1000000
while G.number_of_edges() > 0:
edge = Counter(curEdge).most_common()[0][0]
G.remove_edge(*edge)
curEdge = nx.edge_betweenness(G)
com1 = list(nx.connected_components(G))
mod2 = __modularity__(G1, com1)
if mod2 > mod1:
mod1 = mod2
com2 = com1
return {x: i for i in range(len(com2)) for x in com2[i]}
def dhc(Gc):
H = Gc.copy()
partitions = {}
partition_graph = {}
lvl = 1
# adding the original graph as the first lvl
sub_graphs = nx.connected_component_subgraphs(H)
partitions.update({lvl: [k.nodes() for k in sub_graphs]})
while (nx.number_connected_components(H) != H.number_of_nodes()):
eb = nx.edge_betweenness(
H) # use edge betweenness to find the best edge to remove
sorted_eb = sorted(eb.items(),
reverse=True,
key=operator.itemgetter(1))
k, v = sorted_eb[0][0]
ncc_before = nx.number_connected_components(H)
H.remove_edge(k, v)
ncc_after = nx.number_connected_components(H)
if ncc_before == ncc_after: continue
else:
lvl += 1
sub_graphs = nx.connected_component_subgraphs(H)
partitions.update({lvl: [k.nodes() for k in sub_graphs]})
partition_graph.update({lvl: H.copy()})
return partition_graph, partitions
def HeadTailCommunityDetection(G, finaledgelist):
H = nx.connected_component_subgraphs(G)
for subgraph in H:
result = nx.edge_betweenness(subgraph, False, None)
edges = result.keys()
values = result.values()
mean = getMean(values)
edgelist = []
edgetemp = subgraph.edges()
if len(edgetemp) <= 2:
for edge in edgetemp:
finaledgelist.append(edge)
else:
for index in range(len(values)):
if values[index] <= mean:
edgelist.append(edges[index])
if (
float(len(edgelist)) / float(len(edges))
) <= 0.6: #change the head/tail division rule here, here is for tail percentage, so if the rule is 40/60, the value should be assigned 0.6 as in the code.
for edge in edgelist:
finaledgelist.append(edge)
else:
Gsub = nx.Graph()
for edge in edgelist:
Gsub.add_edge(edge[0], edge[1])
HeadTailCommunityDetection(Gsub, finaledgelist)
def execute(self):
iterTime = 1
while len(self._G.edges()) != 0:
# 找到边介数最大的边，删除边
edge = max(nx.edge_betweenness(self._G).items(), key=lambda item: item[1])[0]
self._G.remove_edge(edge[0], edge[1])
components = [list(c) for c in list(nx.connected_components(self._G))]
cur_Q = 0
if len(components) != len(self._partition):
cur_Q = cal_Q(components, self._G_cloned)
if cur_Q > self._max_Q:
self._max_Q = cur_Q
self._partition = components
iterRound = iterTime
writeClu(self._G, self._partition)
print("该轮结束的划分结果:" + str(self._partition))
print("该轮结束时的模块度Q:" + str(cur_Q))
print("################第" + str(iterTime) + "轮结束##################")
# file_object = open('res/%sres.txt' % str(graphName), 'a')
# file_object.write(str(iterTime) + '|Q：' + str(cur_Q) + '|Result：' + str(self._partition) + '\n')
# file_object.close()
iterTime += 1
print(self._max_Q)
print(self._partition)
def test_networkx_roundtrip(self):
print("\n---------- NetworkX Data Roundtrip Test Start -----------\n")
g = nx.newman_watts_strogatz_graph(100, 3, 0.5)
nodes = g.nodes()
edges = g.edges()
# Add some attributes
g.graph["name"] = "original"
g.graph["density"] = nx.density(g)
nx.set_node_attributes(g, "betweenness", nx.betweenness_centrality(g))
nx.set_node_attributes(g, "degree", nx.degree(g))
nx.set_node_attributes(g, "closeness", nx.closeness_centrality(g))
nx.set_edge_attributes(g, "eb", nx.edge_betweenness(g))
cyjs1 = util.from_networkx(g)
g2 = util.to_networkx(cyjs1)
self.assertEqual(len(g2.nodes()), len(nodes))
self.assertEqual(len(g2.edges()), len(edges))
edge_set = set(list(map(lambda x: (int(x[0]), int(x[1])), g2.edges())))
self.assertEqual(0, len(edge_set.difference(set(edges))))
node_original = g.node[1]
node_generated = g2.node["1"]
print(node_original)
print(node_generated)
self.assertEqual(node_original["degree"], node_generated["degree"])
self.assertEqual(node_original["betweenness"], node_generated["betweenness"])
self.assertEqual(node_original["closeness"], node_generated["closeness"])
def build_graph():
pair_list = TwitterUser.get_top_100_pair()
DG = nx.DiGraph()
DG.add_edges_from([(foer, twitter_user) for twitter_user, foer in
pair_list])
betweenness = nx.betweenness_centrality(DG)
closeness = nx.closeness_centrality(DG)
edge_betweenness = nx.edge_betweenness(DG)
clustering_co = nx.clustering(nx.Graph(DG))
page_rank = nx.pagerank(DG)
for twitter_id in DG.nodes():
t = TwitterUser.get_by_id(twitter_id)
node = DG.node[twitter_id]
node['user_id'] = t.user_id
node['label'] = t.scrn_name
node['follower_count'] = t.foer_cnt
node['friend_count'] = t.friend_cnt
node['status_count'] = t.status_cnt
node['location'] = t.location
node['verified'] = t.verified
node['twitter_age'] = (date.today() - t.created_at).days
node['daily_tweet'] = t.status_cnt*1.0/node['twitter_age']
node['indegree'] = len([(id, foer) for id, foer
in pair_list if id == twitter_id])
node['outdegree'] = len([(id, foer) for id, foer
in pair_list if foer == twitter_id])
node['cluster'] = clustering_co[twitter_id]
node['betweenness'] = betweenness[twitter_id]
node['closeness'] = closeness[twitter_id]
node['page_rank'] = page_rank[twitter_id]
for out_n, in_n in DG.edges():
DG[out_n][in_n]['edge_betweenness'] = edge_betweenness[(out_n,in_n)]
return DG
def test_networkx_roundtrip(self):
print('\n---------- NetworkX Data Roundtrip Test Start -----------\n')
g = nx.newman_watts_strogatz_graph(100, 3, 0.5)
nodes = g.nodes()
edges = g.edges()
# Add some attributes
g.graph['name'] = 'original'
g.graph['density'] = nx.density(g)
nx.set_node_attributes(g, 'betweenness', nx.betweenness_centrality(g))
nx.set_node_attributes(g, 'degree', nx.degree(g))
nx.set_node_attributes(g, 'closeness', nx.closeness_centrality(g))
nx.set_edge_attributes(g, 'eb', nx.edge_betweenness(g))
cyjs1 = util.from_networkx(g)
g2 = util.to_networkx(cyjs1)
self.assertEqual(len(g2.nodes()), len(nodes))
self.assertEqual(len(g2.edges()), len(edges))
edge_set = set(list(map(lambda x: (int(x[0]), int(x[1])), g2.edges())))
self.assertEqual(0, len(edge_set.difference(set(edges))))
node_original = g.node[1]
node_generated = g2.node['1']
print(node_original)
print(node_generated)
self.assertEqual(node_original['degree'], node_generated['degree'])
self.assertEqual(node_original['betweenness'], node_generated['betweenness'])
self.assertEqual(node_original['closeness'], node_generated['closeness'])
def GN_Algorithm(G, G_original):
partition = [[n for n in G.nodes()]]
max_q = 0.0
while len(G.edges()) != 0:
# calculate betweenness
betweenness_dict = nx.edge_betweenness(G)
betweenness_max_edge = max(betweenness_dict.items(),
key=lambda item: item[1])[0]
G.remove_edge(betweenness_max_edge[0], betweenness_max_edge[1])
components = [list(c) for c in list(nx.connected_components(G))]
if (len(components) != len(partition)):
cal_q = calculate_Q(components, G_original)
# We want to fine the biggest modularity value!
if cal_q > max_q:
max_q = cal_q
partition = components
# if do not use Q
'''
if len(components)==num_groups:
partition=components
max_q=calculate_Q(components,G_original)
break
'''
return partition
def girvan_newman_partition(G, partition_count, protected_edges=None):
ranked_betweenness_edges = nx.edge_betweenness(G)
inverse_map = make_inverse_dict(ranked_betweenness_edges)
max_edge_key = max(ranked_betweenness_edges.values())
max_edge_choice = random.choice(range(len(inverse_map[max_edge_key])))
max_edge = inverse_map[max_edge_key][max_edge_choice]
if protected_edges:
ranks = ranked_betweenness_edges.values()
ranks.sort()
ranks.reverse()
if len(ranked_betweenness_edges.keys()) > len(protected_edges):
#imperfect, but faster than computing set differences
max_edge = find_unprotected_edge(ranks, copy.copy(inverse_map),
max_edge, protected_edges)
else:
candidates = list(
set(ranked_betweenness_edges.keys()).difference(
set(protected_edges)))
if len(candidates) > 0:
while max_edge not in candidates:
max_edge = find_unprotected_edge(ranks,
copy.copy(inverse_map),
max_edge, protected_edges)
G.remove_edge(max_edge[0], max_edge[1])
if nx.number_connected_components(G) >= partition_count:
return [max_edge]
else:
return [max_edge] + girvan_newman_partition(G, partition_count,
protected_edges)
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_networkx_roundtrip(self):
print('\n---------- NetworkX Data Roundtrip Test Start -----------\n')
g = nx.newman_watts_strogatz_graph(100, 3, 0.5)
nodes = g.nodes()
edges = g.edges()
# Add some attributes
g.graph['name'] = 'original'
g.graph['density'] = nx.density(g)
nx.set_node_attributes(g, 'betweenness', nx.betweenness_centrality(g))
nx.set_node_attributes(g, 'degree', nx.degree(g))
nx.set_node_attributes(g, 'closeness', nx.closeness_centrality(g))
nx.set_edge_attributes(g, 'eb', nx.edge_betweenness(g))
cyjs1 = util.from_networkx(g)
g2 = util.to_networkx(cyjs1)
self.assertEqual(len(g2.nodes()), len(nodes))
self.assertEqual(len(g2.edges()), len(edges))
edge_set = set(list(map(lambda x: (int(x[0]), int(x[1])), g2.edges())))
self.assertEqual(0, len(edge_set.difference(set(edges))))
node_original = g.node[1]
node_generated = g2.node['1']
print(node_original)
print(node_generated)
self.assertEqual(node_original['degree'], node_generated['degree'])
self.assertEqual(node_original['betweenness'], node_generated['betweenness'])
self.assertEqual(node_original['closeness'], node_generated['closeness'])
def compute_best_community(original_g):
max_modularity = -1
total_nodes = nx.number_of_nodes(original_g)
community_count = 1
g = original_g
communities = []
#Generate all the communities: Loop thru taking the entire graph as 1 community to each node as a seperate community
while community_count < total_nodes:
betweenness = nx.edge_betweenness(g)
max_betweenness = max(betweenness.iteritems(), key = operator.itemgetter(1))[0]
g.remove_edge(max_betweenness[0], max_betweenness[1])
connected_subgraphs = nx.connected_components(g)
connected_subgraphs_list = convert_generator_list(connected_subgraphs)
community_dict = categorize_nodes(connected_subgraphs_list)
modularity = community.modularity(community_dict, original_g)
if modularity > max_modularity:
max_modularity = modularity
communities = list(connected_subgraphs_list)
community_count += 1
communities = format_list(communities)
return communities, max_modularity
def execute(self):
iterTime = 1
while len(self._G.edges()) != 0:
# 找到边介数最大的边，删除边
maxD = -float('Inf')
edgeDic = nx.edge_betweenness(self._G)
for edge in edgeDic:
thisD = edgeDic.get(edge)
if thisD > maxD:
maxD = thisD
removeE = edge
self._G.remove_edge(removeE[0], removeE[1])
components = [
list(c) for c in list(nx.connected_components(self._G))
]
cur_Q = 0
if len(components) != len(self._partition):
cur_Q = cal_Q(components, self._G_cloned)
if cur_Q > self._max_Q:
self._max_Q = cur_Q
self._partition = components
iterRound = iterTime
writeClu(self._G, self._partition)
print("该轮结束的划分结果:" + str(self._partition))
print("该轮结束时的模块度Q:" + str(cur_Q))
print("################第" + str(iterTime) +
"轮结束##################")
iterTime += 1
print("最大Q值出现在：第" + str(iterRound) + "轮。最大Q值为：" + str(self._max_Q))
for clu in self._partition:
print(sorted(clu))
def compute_best_community(original_g):
max_modularity = -1
total_nodes = nx.number_of_nodes(original_g)
community_count = 1
g = original_g
communities = []
# Generate all the communities: Loop thru taking the entire graph as 1 community to each node as a seperate community
while community_count < total_nodes:
betweenness = nx.edge_betweenness(g)
max_betweenness = max(betweenness.iteritems(), key=operator.itemgetter(1))[0]
g.remove_edge(max_betweenness[0], max_betweenness[1])
connected_subgraphs = nx.connected_components(g)
connected_subgraphs_list = convert_generator_list(connected_subgraphs)
community_dict = categorize_nodes(connected_subgraphs_list)
modularity = community.modularity(community_dict, original_g)
if modularity > max_modularity:
max_modularity = modularity
communities = list(connected_subgraphs_list)
community_count += 1
communities = format_list(communities)
return communities, max_modularity
def getEdgeBetweennessList(self):
edgelist = []
edgeBetwness = NX.edge_betweenness(self)
# sort the edges based on betweeness centrality in reverse order
# (highest first)
edgelist = [(edge[0], edge[1], betwcen)
for edge, betwcen in list(edgeBetwness.items())]
return(edgelist)
def getEdgeBetweennessList(self):
edgelist = []
edgeBetwness = NX.edge_betweenness(self)
# sort the edges based on betweeness centrality in reverse order
# (highest first)
edgelist = [(edge[0], edge[1], betwcen)
for edge, betwcen in edgeBetwness.items()]
return(edgelist)
def getresults(graph):
print("Analisando grafo...")
print("Média do grau dos nodos: " + str(nx.average_degree_connectivity(graph)))
print("Coeficiente de clusterização: " + str(nx.average_clustering(graph)))
for g in nx.connected_component_subgraphs(graph):
print("Distância média dos nós: " + str(nx.average_shortest_path_length(g)))
print("Betweenness das arestas: " + str(nx.edge_betweenness(graph)))
print("Betweenness dos nodos: " + str(nx.betweenness_centrality(graph)))
def betweeness_calculation(graph):
betweeness = nx.edge_betweenness(graph,
k=None,
normalized=False,
weight=None,
seed=None)
graph.remove_edge(
*(max(betweeness.iteritems(), key=operator.itemgetter(1))[0]))
return graph
def get_max_bt_edge(g):
bt = networkx.edge_betweenness(g) # 获得边的betweenness
a = 0.0
b = (0, 0)
for i1 in bt.items():
if i1[1] >= a:
a = i1[1]
b = i1[0]
return b
def betweeness_calculation(graph):
betweeness = nx.edge_betweenness(graph, k=None, normalized=False, weight=None, seed=None)
#print betweeness
maxval = max(betweeness.iteritems(), key=operator.itemgetter(1))[1]
keys = [k for k,v in betweeness.items() if v==maxval]
#print keys
for edge in keys:
graph.remove_edge(*edge)
#print max(betweeness.iteritems(), key=operator.itemgetter(1))[0]
return graph
def get_pre_recall():
while len(self._G1.edges()) != 0:
edge = max(nx.edge_betweenness(self._G1).items(), key=lambda item: item[1])[0]
self._G1.remove_edge(edge[0], edge[1])
components = [list(c) for c in list(nx.connected_components(self._G1))]
if len(components) != len(self._partition):
cur_Q = cal_Q(components, self._G1)
if cur_Q > self._max_Q:
self._max_Q = cur_Q
self._partition = components
def is_community(component):
if len(component.nodes())<6:
return True
else:
max = 0
for edge,value in nx.edge_betweenness(component,normalized=False).iteritems():
if value > max:
max = value
if max <= len(component.nodes())-1:
return True
else:
return False
def _getBetweenCentralityGraph(self):
'''
returns a graph made of same nodes and edges as 'self' but weights of edges
are replaced by 'edge betweenness centrality'
'''
centralityGraph = self.__class__()
edgeBetwness = NX.edge_betweenness(self)
for edge, betwnness in edgeBetwness.items():
u = edge[0]
v = edge[1]
centralityGraph.add_edge(u, v, betwnness)
return(centralityGraph)
def girvan_newman(G):
ranked_betweenness_edges = nx.edge_betweenness(G)
inverse_map = make_inverse_dict(ranked_betweenness_edges)
max_edge_key = max(ranked_betweenness_edges.values())
max_edge_choice = random.choice(range(len(inverse_map[max_edge_key])))
max_edge = inverse_map[max_edge_key][max_edge_choice]
G.remove_edge(max_edge[0], max_edge[1])
if len(G.edges()) <= 2:
return [max_edge] + G.edges()
else:
return [max_edge] + girvan_newman(G)
def _getBetweenCentralityGraph(self):
'''
returns a graph made of same nodes and edges as 'self' but weights of edges
are replaced by 'edge betweenness centrality'
'''
centralityGraph = self.__class__()
edgeBetwness = NX.edge_betweenness(self)
for edge, betwnness in list(edgeBetwness.items()):
u = edge[0]
v = edge[1]
centralityGraph.add_edge(u, v, betwnness)
return(centralityGraph)
def Kernighan_Lin(self):
edges = list(self.G.edges.data())
A_nodes = list(self.G.nodes)
B_nodes = []
if len(A_nodes) % 2 != 0:
A_nodes.append("")
for i in range(int(len(A_nodes) / 2)):
B_nodes.append(A_nodes.pop())
print(A_nodes)
print(B_nodes)
print(edges)
print(edge_betweenness_centrality(self.G, True))
print(edge_betweenness(self.G, True))
def execute(self):
while len(self._G.edges()) != 0:
edge = max(nx.edge_betweenness(self._G).items(),key=lambda item:item[1])[0]
self._G.remove_edge(edge[0], edge[1])
components = list(nx.connected_components(self._G))
if len(components) != len(self._partition):
cur_Q = cal_Q(components, self._G_cloned)
if cur_Q > self._max_Q:
self._max_Q = cur_Q
self._partition = components
print self._max_Q
print self._partition
return self._partition
def test_networkx_digraph_edge_attr(self):
print('\n---------- Digraph Edge Att Test Start -----------\n')
g = nx.DiGraph()
g.add_path([0, 1, 2, 3, 4])
eb = nx.edge_betweenness(g)
nx.set_edge_attributes(g, 'eb', eb)
cyjs = util.from_networkx(g)
print(json.dumps(cyjs, indent=4))
# There is only one edge, so this should be OK...
edge = cyjs['elements']['edges'][0]
self.assertEqual(3, len(edge['data']))
def test_networkx_digraph_edge_attr(self):
print('\n---------- Digraph Edge Att Test Start -----------\n')
g = nx.DiGraph()
g.add_path([0, 1, 2, 3, 4])
eb = nx.edge_betweenness(g)
nx.set_edge_attributes(g, 'eb', eb)
cyjs = util.from_networkx(g)
print(json.dumps(cyjs, indent=4))
# There is only one edge, so this should be OK...
edge = cyjs['elements']['edges'][0]
self.assertEqual(3, len(edge['data']))
def betweeness_calculation(graph):
betweeness = nx.edge_betweenness(graph,
k=None,
normalized=False,
weight=None,
seed=None)
#print betweeness
maxval = max(betweeness.iteritems(), key=operator.itemgetter(1))[1]
keys = [k for k, v in betweeness.items() if v == maxval]
#print keys
for edge in keys:
graph.remove_edge(*edge)
#print max(betweeness.iteritems(), key=operator.itemgetter(1))[0]
return graph
def execute(self):
while len(self._G.edges()) != 0:
edge = max(nx.edge_betweenness(self._G).items(),
key=lambda item: item[1])[0]
self._G.remove_edge(edge[0], edge[1])
components = list(nx.connected_components(self._G))
if len(components) != len(self._partition):
cur_Q = cal_Q(components, self._G_cloned)
if cur_Q > self._max_Q:
self._max_Q = cur_Q
self._partition = components
print self._max_Q
print self._partition
return self._partition
def execute(self):
while len(self._G.edges()) != 0:
edge = max(nx.edge_betweenness(self._G).items(),
key=lambda item: item[1])[0]
self._G.remove_edge(edge[0], edge[1])
components = [
list(c) for c in list(nx.connected_components(self._G))
]
if len(components) != len(self._partition):
cur_Q = modularity(self._G_cloned, components)
if cur_Q > self._max_Q:
self._max_Q = cur_Q
self._partition = components
print(self._max_Q)
print(self._partition)
return self._partition
def execute(self):
while len(self._G.edges()) > 0:
# 1.计算所有边的edge betweenness
edge = max(nx.edge_betweenness(self._G).items(),
key = lambda item:item[1])[0]
# 2.移去edge betweenness最大的边
self._G.remove_edge(edge[0], edge[1])
# 获得移去边后的子连通图
components = [list(c) for c in list(nx.connected_components(self._G))]
if len(components) != len(self._partition):
# 3.计算Q值
cur_Q = cal_Q(components, self._G_cloned)
if cur_Q > self._max_Q:
self._max_Q = cur_Q
self._partition = components
print("max Q:", self._max_Q)
return self._partition
def get_recall(f, G1,G2,pre, recall):
G_cloned = G1.copy()
G_tmp = G2.copy()
partition = [[n for n in G.nodes()]]
max_Q = 0.0
max_Q = round()
while len(G_tmp.edges()) != 0:
edge = max(nx.edge_betweenness(G_tmp).items(),key=lambda item:item[1])[0]
G_tmp.remove_edge(edge[0], edge[1])
components = [list(c) for c in list(nx.connected_components(G_tmp))]
if len(components) != len(partition):
components_tmp = list2dict(components)
cur_Q = community.modularity(components_tmp, G_cloned, weight='weight')
if cur_Q > max_Q:
max_Q = cur_Q
partition = components
G = nx.MultiGraph()
head = f.readline().split()
line = f.readline().split()
mapdict = dict()
pre=0
recall=0
TP_FP= 0
TP = 0
TP_FN = 0
while line:
NS(mapdict, line[2], line[0])
line = f.readline().split()
# 统计社区匹配用户IP的数量，进行跨社交网络社区匹配
for IP in mapdict.keys():
nodes = list(mapdict[IP].keys())
for i in range(len(nodes)):
for j in range(i + 1, len(nodes)):
num = G.number_of_edges(nodes[i], nodes[j])
G.add_edge(nodes[i], nodes[j])
G[nodes[i]][nodes[j]][num]["share_IP"] = IP
G[nodes[i]][nodes[j]][num]["coun"] = min(mapdict[IP][str(nodes[i])], mapdict[IP][str(nodes[j])])
TP = TP + 1
TP_FN = TP_FN + 1
pre = TP / TP_FP
recall = TP / TP_FN
def computeSubgraphClusters(G,start=0,divisions=2):
edged_subgraphs = len(nx.connected_component_subgraphs(G))
while edged_subgraphs < divisions:
for pair in reversed(sorted(nx.edge_betweenness(G).iteritems(), key=itemgetter(1))[-1:]):
G.remove_edge(pair[0][0], pair[0][1])
new_subgraphs = nx.connected_component_subgraphs(G)
proposed_subgraphs = []
for subgraph in new_subgraphs:
if len(subgraph.nodes()) > 1:
proposed_subgraphs.append(subgraph)
else:
break
#if we start losing clusters, stop
#no guarantee we haven't hit a local maximum
if len(proposed_subgraphs) < edged_subgraphs:
break
subgraphs = proposed_subgraphs
edged_subgraphs = len(subgraphs)
print edged_subgraphs
for index, subgraph in enumerate(subgraphs):
s0 = nx.Graph()
s0.add_edges_from(subgraph.edges(data=True))
print
print subgraph.nodes(data=True)
# attrs = ["fillcolor", "style", "shape", "fontsize", "fontname", "fontcolor"]
# for node in subgraph.nodes(data=True):
# node_attr = {}
# for attr in attrs:
# node_attr[attr] = node[1][attr]
#
# s0.node[node[0]] = node_attr
#print s0.nodes(data=True)
noteSubgraph = nx.to_agraph(s0)
#print subgraph
#print noteSubgraph.node_attr
#noteSubgraph.graph_attr.update(bgcolor="#ffffff", overlap="scale", splines="true", size="10.5,8", dpi="96", maxiter="2400") #noteSubgraph.node_attr.update(fontsize="15", color="#666666", style="filled", fillcolor=colors[data[0]], fontcolor="white", fontname="Tuffy", shape="box")
#noteSubgraph.layout(prog="neato")
#noteSubgraph.draw("noteSubgraph%d.png" % (index + 1 + start), format="png")
return G,subgraphs
def test(G_orig, number_of_clusters, clusters):
G = deepcopy(G_orig)
start = time.time()
pos = nx.spring_layout(G)
# print(len(G.edges))
number_of_current_clusters = nx.number_connected_components(G)
plt.figure()
nx.draw(G_orig, pos=pos, node_color=clusters)
plt.title('Correct labels')
plt.show()
k = len(G.nodes)
if len(G.edges) > 1000:
k = round(len(G.nodes) * 0.1)
while number_of_current_clusters < number_of_clusters:
# if len(G.edges) % 10 == 0:
# print(len(G.edges))
# print(number_of_current_clusters)
#plt.figure()
#nx.draw(G, pos=pos)
#plt.show()
edge_centrality = nx.edge_betweenness(G, k)
u, v = max(edge_centrality, key=lambda key: edge_centrality[key])
G.remove_edge(u, v)
number_of_current_clusters = nx.number_connected_components(G)
# print(len(G.edges))
# print(number_of_current_clusters)
res = [c for c in nx.connected_components(G)]
our_clusters = []
for node in G.nodes:
for i in range(len(res)):
if node in res[i]:
our_clusters.append(i)
break
# print(our_clusters)
plt.cla()
plt.title('Clustering result')
nx.draw(G_orig, pos=pos, node_color=our_clusters)
plt.show()
end = time.time()
# print(end - start)
return G
def custom_remove_edge(component):
#remove edge
removed_edge = None
#check if broken
max = 0
for current_edge, edge_betweenness_value in nx.edge_betweenness(component).iteritems():
if edge_betweenness_value > max:
removed_edge = current_edge
max = edge_betweenness_value
#Find edge with this value, remove it
component.remove_edge(removed_edge[0],removed_edge[1])
if removed_edge is None:
raise ValueError('Removed edge should not be None')
count = 0
for component in list(nx.connected_component_subgraphs(component)):
count += 1
if count >= 2:
return True,removed_edge
else:
return False,removed_edge
def RecalculatedEdgeBetweennessAttack(G, remove_fraction = 1.0):
""" Recalculated Edge Betweenness Attack
"""
n = G.number_of_nodes()
m = int(G.number_of_edges() * (remove_fraction + 0.0))
tot_ND = [0] * (m + 1)
tot_T = [0] * (m + 1)
ND, ND_lambda = ECT.get_number_of_driver_nodes(G)
tot_ND[0] = ND
tot_T[0] = 0
for i in range(m):
all_edgeBetweenness = nx.edge_betweenness(G)
(u, v) = max(all_edgeBetweenness, key = all_edgeBetweenness.get)
G.remove_edge(u, v)
ND, ND_lambda = ECT.get_number_of_driver_nodes(G)
tot_ND[i+1] = ND
tot_T [i+1] = i + 1
return (tot_ND, tot_T)
def InitialEdgeBetweennessAttack(G, remove_fraction = 1.0):
""" Initial Edge Betweenness Attack
"""
n = G.number_of_nodes()
m = int(G.number_of_edges() * (remove_fraction + 0.0))
tot_ND = [0] * (m + 1)
tot_T = [0] * (m + 1)
ND, ND_lambda = ECT.get_number_of_driver_nodes(G)
tot_ND[0] = ND
tot_T[0] = 0
all_edgeBetweenness = nx.edge_betweenness(G)
sorted_betweenness = sorted(all_edgeBetweenness.items(), key = operator.itemgetter(1), reverse=True)
for i in range(m):
(u, v), b = sorted_betweenness[i]
G.remove_edge(u, v)
ND, ND_lambda = ECT.get_number_of_driver_nodes(G)
tot_ND[i+1] = ND
tot_T[i+1] = i + 1
return (tot_ND, tot_T)
def dhc(Gc):
H = Gc.copy()
partitions = {}
partition_graph = {}
lvl = 1
# adding the original graph as the first lvl
sub_graphs = nx.connected_component_subgraphs(H)
partitions.update({lvl:[k.nodes() for k in sub_graphs]})
while(nx.number_connected_components(H)!=H.number_of_nodes()):
eb = nx.edge_betweenness(H) # use edge betweenness to find the best edge to remove
sorted_eb = sorted(eb.items(), reverse=True, key=operator.itemgetter(1))
k,v = sorted_eb[0][0]
ncc_before = nx.number_connected_components(H)
H.remove_edge(k,v)
ncc_after = nx.number_connected_components(H)
if ncc_before == ncc_after: continue
else:
lvl +=1
sub_graphs = nx.connected_component_subgraphs(H)
partitions.update({lvl:[k.nodes() for k in sub_graphs]})
partition_graph.update({lvl:H.copy()})
return partition_graph, partitions
def HeadTailCommunityDetection(G,finaledgelist):
H=nx.connected_component_subgraphs(G)
for subgraph in H:
result=nx.edge_betweenness(subgraph, False, None)
edges=result.keys()
values=result.values()
mean = getMean(values)
edgelist=[]
edgetemp=subgraph.edges();
if len(edgetemp)<=2:
for edge in edgetemp:
finaledgelist.append(edge)
else:
for index in range(len(values)):
if values[index] <= mean:
edgelist.append(edges[index])
if (float(len(edgelist))/float(len(edges)))<=0.6: #change the head/tail division rule here, here is for tail percentage, so if the rule is 40/60, the value should be assigned 0.6 as in the code.
for edge in edgelist:
finaledgelist.append(edge)
else:
Gsub= nx.Graph()
for edge in edgelist:
Gsub.add_edge(edge[0],edge[1])
HeadTailCommunityDetection(Gsub,finaledgelist)
def __HeadTailCommunityDetection(G, finaledgelist, head_tail_ratio=0.6):
H = nx.connected_components(G)
for s in H:
subgraph = nx.subgraph(G, s)
result = nx.edge_betweenness(subgraph, normalized=False)
edges = list(result.keys())
values = list(result.values())
mean = np.mean(values)
edgelist = []
edgetemp = subgraph.edges()
if len(edgetemp) <= 2:
for edge in edgetemp:
finaledgelist.append(edge)
else:
for index in range(len(values)):
if values[index] <= mean:
edgelist.append(edges[index])
if float(len(edgelist)) / float(
len(edges)
) <= head_tail_ratio: # change the head/tail division rule here, here is for tail percentage,
# so if the rule is 40/60, the value should be assigned 0.6 as in the code.
for edge in edgelist:
finaledgelist.append(edge)
else:
Gsub = nx.Graph()
for edge in edgelist:
Gsub.add_edge(edge[0], edge[1])
try:
__HeadTailCommunityDetection(Gsub, finaledgelist,
head_tail_ratio)
except:
pass
return finaledgelist
def GN(G):
G_cloned = G.copy()
G_tmp = G.copy()
partition = [[n for n in G.nodes()]]
max_Q = 0.0
while len(G_tmp.edges()) != 0:
edge = max(nx.edge_betweenness(G_tmp).items(),
key=lambda item: item[1])[0]
G_tmp.remove_edge(edge[0], edge[1])
components = [list(c) for c in list(nx.connected_components(G_tmp))]
# print( len(G_tmp.edges()))
if len(components) != len(partition):
components_tmp = list2dict(components)
cur_Q = community.modularity(components_tmp,
G_cloned,
weight='weight')
# print(cur_Q)
if cur_Q > max_Q:
max_Q = cur_Q
partition = components
print("max_Q = ", max_Q)
# print ("partitions are: ", partition)
return partition
# writer.writerows(zip(T, ND))
# ND4, T4 = InitialEdgeDegreeAttack(G4)
# with open("results/test4.csv", "w") as f:
# writer = csv.writer(f, delimiter=',')
# writer.writerows(zip(T, ND))
# ND5, T5 = RecalculatedEdgeDegreeAttack(G5)
# with open("results/test5.csv", "w") as f:
# writer = csv.writer(f, delimiter=',')
# writer.writerows(zip(T, ND))
G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (2, 3)])
d = G.degree(G.nodes())
b = nx.edge_betweenness(G)
print d
print b
d = {}
for u, v in G.edges():
edge_degree = G.degree(u) * G.degree(v)
d[(u, v)] = edge_degree
print 'initial degrees:', d
sorted_degrees = sorted(d.items(), key = operator.itemgetter(1), reverse=True)
print 'sorted_degrees:', sorted_degrees
G1 = nx.barabasi_albert_graph(200, 3)
print 'edge num:', G1.number_of_edges()
def edgebet(net):
return distri(nx.edge_betweenness(net, normalized = True).values(),'betweenness')
def construct_ccig(sentences, concepts, title=None, use_cd=True, betweenness_threshold_coef=1.0, max_c_size=10,
min_c_size=3, IDF=None):
"""
Given a segmented text and a list of concepts,
construct concept community interaction graph.
:param sentences: a list of sentences.
:param concepts: a list of concepts.
:return: a concept community interaction graph.
"""
g = nx.Graph()
concepts = list(set(concepts))
concepts = remove_values_from_list(concepts, EMPTY_VERTEX_NAME)
if len(sentences) == 0 or len(concepts) == 0:
print("No concept in concepts list.")
return None
if len(concepts) > 70:
print("Too many concepts.")
return None
# get concept communities
if use_cd:
concept_communities = get_concept_communities(sentences, concepts, betweenness_threshold_coef, max_c_size,
min_c_size)
else:
concept_communities = [[c] for c in concepts]
if use_cd:
cname_sentidxs = assign_sentences_to_concept_communities(
sentences, concept_communities, IDF)
else:
cname_sentidxs = assign_sentences_to_concepts(sentences, concepts)
# initialize vertex properties
concept_vertexidxs_map = {}
for c in concepts:
concept_vertexidxs_map[c] = []
g.add_node(0, name=EMPTY_VERTEX_NAME, concepts=[], sentidxs=cname_sentidxs[EMPTY_VERTEX_NAME])
# g.add_node(0)
# g.node[0]['name'] = EMPTY_VERTEX_NAME
# g.node[0]['concepts'] = []
# g.node[0]['sentidxs'] = cname_sentidxs[EMPTY_VERTEX_NAME]
# print(g.node[0])
i = 1
for community in concept_communities:
cname = community2name(community)
if len(cname_sentidxs[cname]) == 0:
continue
g.add_node(i, name=cname, concepts=community, sentidxs=cname_sentidxs[cname])
for concept in community:
concept_vertexidxs_map[concept].append(i)
i = i + 1
# edges by connective entences
# dic
eprop_name = {}
eprop_concepts = {}
eprop_sentidxs = {}
eprop_weight_numsent = {}
eprop_weight_tfidf = {}
for sent_idx in range(len(sentences)):
sent = sentences[sent_idx]
words = str(sent).split()
intersect = set(words).intersection(set(concepts))
if len(intersect) == 0:
continue
related_vertexidxs = []
for c in intersect:
related_vertexidxs.extend(concept_vertexidxs_map[c])
related_vertexidxs = list(set(related_vertexidxs))
# print("related_vertex_idx:")
# print(related_vertexidxs)
num_related_v = len(related_vertexidxs)
if num_related_v < 2:
continue
for j in range(num_related_v):
v1_idx = related_vertexidxs[j]
for k in range(j, num_related_v):
if j == k:
continue
v2_idx = related_vertexidxs[k]
source_idx = min(v1_idx, v2_idx)
target_idx = max(v1_idx, v2_idx)
e = (source_idx, target_idx)
if not g.has_edge(source_idx, target_idx):
# g.add_edge(source_idx, target_idx)
eprop_sentidxs[e] = [sent_idx]
eprop_concepts[e] = list(intersect)
g.add_edge(source_idx, target_idx)
# g.add_edges_from([(source_idx, target_idx, dict(sentidxs=eprop_sentidxs[e])),
# (source_idx, target_idx, dict(concepts=eprop_concepts[e]))])
else:
old_idxs = list(eprop_sentidxs[e])
old_idxs.append(sent_idx)
eprop_sentidxs[e] = old_idxs
old_concepts = list(eprop_concepts[e])
old_concepts.extend(intersect)
eprop_concepts[e] = list(set(old_concepts))
g[source_idx][target_idx]['sentidxs'] = eprop_sentidxs[e]
g[source_idx][target_idx]['concepts'] = eprop_concepts[e]
# assign vertex names and weights
for e in g.edges():
eprop_name[e] = " ".join(eprop_concepts[e])
eprop_weight_numsent[e] = float(len(eprop_sentidxs[e]))
eprop_weight_tfidf[e] = 0.0
g[e[0]][e[1]]['weight_numsent'] = eprop_weight_numsent[e]
g[e[0]][e[1]]['weight_tfidf'] = eprop_weight_tfidf[e]
# edges by node text similarity
WEIGHT_THRESHOLD = 0.001 # NOTICE: smaller threshold leads to more edges
numv = g.number_of_nodes()
for i in range(numv):
for j in range(i, numv):
if j == i:
continue
v1 = g.node[i]
v2 = g.node[j]
idxs1 = list(set(v1['sentidxs']))
idxs2 = list(set(v2['sentidxs']))
text1 = [sentences[s] for s in idxs1]
text1 = " ".join(text1)
text2 = [sentences[s] for s in idxs2]
text2 = " ".join(text2)
w = tfidf_cos_sim(text1, text2, IDF)
if w >= WEIGHT_THRESHOLD:
e = (i, j)
if not g.has_edge(i, j):
eprop_sentidxs[e] = []
eprop_concepts[e] = []
eprop_weight_numsent[e] = 0.0
eprop_name[e] = ""
g.add_edges_from([
(i, j, dict(sentidxs=eprop_sentidxs[e])),
(i, j, dict(concepts=eprop_concepts[e])),
(i, j, dict(weight_numsent=eprop_weight_numsent[e])),
(i, j, dict(weight_name=eprop_name[e]))
])
eprop_weight_tfidf[e] = w
g[i][j]['weight_tfidf'] = eprop_weight_tfidf[e]
if title is not None:
g.add_nodes_from('TITLE', name=TITLE_VERTEX_NAME, sentidxs=[], concepts=[])
#g.add_nodes_from('T', name=TITLE_VERTEX_NAME, sentidxs=[], concepts=[])
# calculate vertex scores
pr = nx.pagerank(g, weight='weight_tfidf')
bt = nx.betweenness_centrality(g, weight='weight_tfidf')
#print(bt)
try:
katz = nx.katz_centrality(g, weight='weight_tfidf')
except:
katz = [0.0 for i in range(numv)]
#numv = len(pr)
for i in g.nodes():
#print(i)
g.node[i]['pagerank'] = pr[i]
g.node[i]['betweenness'] = bt[i]
g.node[i]['katz'] = katz[i]
ebt = nx.edge_betweenness(g, weight='weight_tfidf')
#print(ebt)
#print(g.nodes())
for i in range(len(g.nodes())):
for j in range(i, len(g.nodes())):
if j == i:
continue
if g.has_edge(i, j):
g[i][j]['betweenness'] = ebt[(i, j)]
return g
def get_betweenness(graph):
return nx.edge_betweenness(graph)
def test_edge_betweenness(self):
hg_bc = nx.edge_betweenness(self.hg)
g_bc = nx.edge_betweenness(self.g)
self.assertEqual(hg_bc, g_bc)
def betweeness_calculation(graph):
betweeness = nx.edge_betweenness(graph, k=None, normalized=False, weight=None, seed=None)
graph.remove_edge(*(max(betweeness.iteritems(), key=operator.itemgetter(1))[0]))
return graph
def edgebet(net):
return distri(
nx.edge_betweenness(net, normalized=True).values(), 'betweenness')
code = set()
net_file.readline()
for line in net_file.readlines():
flight = line.split(",")
name1 = flight[1].split("\"")
name2 = flight[2].split("\"")
if float(flight[0]) > 400:
G.add_edge(name1[1], name2[1], weight=float(flight[0]) / 365)
G_un.add_edge(name1[1], name2[1])
if name1[1] not in code:
code.add(name1[1])
if name2[1] not in code:
code.add(name2[1])
between = net.edge_betweenness(G_un)
be = net.edge_betweenness(G)
jaccard = net.jaccard_coefficient(G)
locals = dict()
weight = dict()
for city in code:
weight[city] = 0
for s, e in G.edges():
weight[e] += G.get_edge_data(s, e)['weight']
weight[s] += G.get_edge_data(s, e)['weight']
jacc = dict()
for u, v, j in jaccard:
jacc[(u, v)] = j
print(G.degree())
degree_diff = dict()
