forked from snail1996/CommunityDetection
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GN_detecting.py
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GN_detecting.py
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# 首先导入包
import networkx as nx
import matplotlib.pyplot as plt
# 克隆
def clone_graph(G):
cloned_graph = nx.Graph()
for edge in G.edges():
cloned_graph.add_edge(edge[0], edge[1])
return cloned_graph
# 计算Q值
def cal_Q(partition, G):
m = len(list(G.edges()))
a = []
e = []
# 计算每个社区的a值
for community in partition:
t = 0
for node in community:
t += len(list(G.neighbors(node)))
a.append(t / float(2 * m))
# 计算每个社区的e值
for community in partition:
t = 0
for i in range(len(community)):
for j in range(len(community)):
if i != j:
if G.has_edge(community[i], community[j]):
t += 1
e.append(t / float(2 * m))
# 计算Q
q = 0
for ei, ai in zip(e, a):
q += (ei - ai ** 2)
return q
class GN(object):
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
# GN算法
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
# 可视化划分结果
if __name__ == '__main__':
# 加载网络并可视化
G = nx.read_gml("homework_test.gml")
T = nx.read_gml("homework_test.gml")
#pos = nx.kamada_kawai_layout(T) #该布局需要下载额外的包scipy，同次运行时图布局不变
pos = nx.spring_layout(T)
# GN算法
algo = GN(G)
partition = algo.execute()
#partition = [['2', '8', '13', '22', '1', '12', '14', '4', '20', '18'], ['32', '29', '25', '26', '3', '28', '10'], ['17', '6', '11', '7', '5'], ['15', '19', '21', '34', '23', '16', '27', '33', '31', '24', '30', '9']]
print("community number:", len(partition))
for i, nodes in zip(range(len(partition)), partition):
print("community %d:" % i, nodes)
#max_Q = nx.algorithms.community.quality.modularity(T, partition)
#print("max Q:", max_Q)
# plotting
plt.clf()
colors = ['r', 'g', 'b', 'c', 'y', 'm']
nx.draw(T, pos=pos, node_color='bisque', with_labels=True)
for i in range(len(partition)):
nx.draw(T, pos=pos, nodelist=partition[i], node_color=colors[i], node_shape='o')
plt.show()