def gn(dataset_path):
G = load_graph(dataset_path)
algorithm = GN(G)
a = algorithm.execute() # a = np.array(algorithm.execute())
gn_pred = [5] * 1000
for i in range(len(a)):
for j in range(len(a[i])):
gn_pred[a[i][j] - 1] = i
return gn_pred
class GN:
def __init__(self, G):
self._G_cloned = clone_graph(G)
self._G = G
self._partition = [[n for n in G.nodes()]]
self._max_Q = 0.0
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
if __name__ == '__main__':
G = load_graph('../network/club.txt')
algorithm = GN(G)
algorithm.execute()
#print len(G.edges(None, False))
class GN:
def __init__(self, G):
self._G_cloned = clone_graph(G)
self._G = G
self._partition = [[n for n in G.nodes()]]
self._max_Q = 0.0
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 = 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
if __name__ == '__main__':
G = load_graph('../network/club.txt')
algorithm = GN(G)
algorithm.execute()
# print len(G.edges(None, False))
import sys
sys.path.append('../../')
sys.path.append('../../algorithm')
from algorithm import LPA
from util import graph_helper
import time
if __name__ == '__main__':
G = graph_helper.load_graph('staticInOut1.csv')
max_iter = 2000000 #默认200
algorithm = LPA.LPA(G, max_iter)
start = time.time()
communities = algorithm.execute()
end = time.time()
print "Algorithm done in " + str(end - start) + "s."
community_id = 1
with open("vertices_LPA_1.csv", 'w') as FILEOUT:
print >> FILEOUT, "Id,commnuity_id"
for cset in communities:
for c in cset:
print >> FILEOUT, str(c) + "," + str(community_id)
community_id = community_id + 1
print "Done"
clique_neighbor[j].add(i)
# depth first search clique neighbors for communities
communities = []
for i, c in enumerate(cliques):
if i in remained and len(c) >= self._k:
#print 'remained cliques', len(remained)
communities.append(set(c))
neighbors = list(clique_neighbor[i])
while len(neighbors) != 0:
n = neighbors.pop()
if n in remained:
#if len(remained) % 100 == 0:
#print 'remained cliques', len(remained)
communities[len(communities) - 1].update(cliques[n])
remained.remove(n)
for nn in clique_neighbor[n]:
if nn in remained:
neighbors.append(nn)
return communities
if __name__ == '__main__':
G = load_graph('../network/community.txt')
algorithm = CPM(G, 4)
communities = algorithm.execute()
for community in communities:
print(community)
nx.draw_networkx(G, with_labels=False)
plt.show()
for i in range(self._n):
c_id = 0
cur_max = q[i][0]
for j in range(1, self._k):
if q[i][j] > cur_max:
cur_max = q[i][j]
c_id = j
communities[c_id].add(i)
return communities.values()
if __name__ == '__main__':
path_list = [
'../network/club.txt', '../../Dataset/facebook/facebook_combined.txt'
]
graph_path = path_list[0]
G = load_graph(graph_path)
# G = nx.karate_club_graph()
algorithm = EM(G, 2)
start_time = time.time()
communities = algorithm.execute()
execution_time = (time.time() - start_time)
time_length = '{0:.2f} s'.format(execution_time)
print(time_length)
for c in communities:
print(len(c))
print(sorted(c))