1 要安装的包
使用community安装python-louvain即可
pip install python-louvain
pip install networkx
2、描述
lst1=
[ [0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 1, 0, 1],
[0, 0, 0, 0, 0, 1, 1, 0] ]
由邻接矩阵lst1生成如下无向图
再根据louvain算法进行分区得到下图
3、程序
import numpy as np
import community.community_louvain
import networkx as nx
import matplotlib.pyplot as plt
#图类
class Graph_Matrix:
"""
Adjacency Matrix
"""
def __init__(self, vertices=[], matrix=[]):
"""
:param vertices:a dict with vertex id and index of matrix , such as {vertex:index}
:param matrix: a matrix
"""
self.matrix = matrix
self.edges_dict = {
} # {(tail, head):weight}
self.edges_array = [] # (tail, head, weight)
self.vertices = vertices
self.num_edges = 0
# if provide adjacency matrix then create the edges list
if len(matrix) > 0:
if len(vertices) != len(matrix):
raise IndexError
self.edges = self.getAllEdges()
self.num_edges = len(self.edges)
# if do not provide a adjacency matrix, but provide the vertices list, build a matrix with 0
elif len(vertices) > 0:
self.matrix = [[0 for col in range(len(vertices))] for row in range(len(vertices))]
self.num_vertices = len(self.matrix)
def isOutRange(self, x):
try:
if x >= self.num_vertices or x <= 0:
raise IndexError
except IndexError:
print("节点下标出界")
def isEmpty(self):
if self.num_vertices == 0:
self.num_vertices = len(self.matrix)
return self.num_vertices == 0
def add_vertex(self, key):
if key not in self.vertices:
self.vertices[key] = len(self.vertices) + 1
# add a vertex mean add a row and a column
# add a column for every row
for i in range(self.getVerticesNumbers()):
self.matrix[i].append(0)
self.num_vertices += 1
nRow = [0] * self.num_vertices
self.matrix.append(nRow)
def getVertex(self, key):
pass
def add_edges_from_list(self, edges_list): # edges_list : [(tail, head, weight),()]
for i in range(len(edges_list)):
self.add_edge(edges_list[i][0], edges_list[i][1], edges_list[i][2], )
def add_edge(self, tail, head, cost=0):
# if self.vertices.index(tail) >= 0:
# self.addVertex(tail)
if tail not in self.vertices:
self.add_vertex(tail)
# if self.vertices.index(head) >= 0:
# self.addVertex(head)
if head not in self.vertices:
self.add_vertex(head)
# for directory matrix
self.matrix[self.vertices.index(tail)][self.vertices.index(head)] = cost
# for non-directory matrix
# self.matrix[self.vertices.index(fromV)][self.vertices.index(toV)] = \
# self.matrix[self.vertices.index(toV)][self.vertices.index(fromV)] = cost
self.edges_dict[(tail, head)] = cost
self.edges_array.append((tail, head, cost))
self.num_edges = len(self.edges_dict)
def getEdges(self, V):
pass
def getVerticesNumbers(self):
if self.num_vertices == 0:
self.num_vertices = len(self.matrix)
return self.num_vertices
def getAllVertices(self):
return self.vertices
def getAllEdges(self):
for i in range(len(self.matrix)):
for j in range(len(self.matrix)):
if 0 < self.matrix[i][j] < float('inf'):
self.edges_dict[self.vertices[i], self.vertices[j]] = self.matrix[i][j]
self.edges_array.append([self.vertices[i], self.vertices[j], self.matrix[i][j]])
return self.edges_array
def __repr__(self):
return str(''.join(str(i) for i in self.matrix))
def to_do_vertex(self, i):
print('vertex: %s' % (self.vertices[i]))
def to_do_edge(self, w, k):
print('edge tail: %s, edge head: %s, weight: %s' % (self.vertices[w], self.vertices[k], str(self.matrix[w][k])))
if __name__=='__main__':
th1 = np.array(
[
[0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 1, 0, 1],
[0, 0, 0, 0, 0, 1, 1, 0]
])
#根据邻接矩阵生成图
nodes=[i for i in range(8)]
my_graph = Graph_Matrix(nodes, th1)
G = nx.Graph() # 建立一个空的无向图G
#将点和邻接关系加入到图中
for node in my_graph.vertices:
G.add_node(str(node))
for edge in my_graph.edges:
G.add_edge(str(edge[0]), str(edge[1]))
#根据louvain算法计算最佳分区
partition = community.community_louvain.best_partition(G)
size = float(len(set(partition.values())))
pos = nx.spring_layout(G)
count = 0.
for com in set(partition.values()) :
count = count + 1.
list_nodes = [nodes for nodes in partition.keys()
if partition[nodes] == com]
nx.draw_networkx_nodes(G, pos, list_nodes, node_size = 20,
node_color = str(count / size))
#绘制
#nx.draw_networkx_edges(G,pos, alpha=0.5, edge_color='#00649a')
nx.draw_networkx_edges(G,pos, width=1.0,edge_color='k',style='solid',alpha=None)
plt.show()
参考文章如下：
Python社区发现—Louvain—networkx和community
Python 邻接矩阵实现无向图、有向图的三种方法，并绘图显示