1.四個文件

cat  A.關系圖.xlsx
Source    Target    Weight
A    B    6
A    C    6
A    D    7
A    E    7
A    F    6
A    G    5
A    H    5
A    I    3
A    J    6

cat B.關系圖.xlsx
Source    Target    Weight
B    C    7
B    D    7
B    E    5
B    J    6
C    D    8

cat C.關系圖.xlsx
Source    Target    Weight
C    B    
 
7
C    E    8
C    A    8
C    K    5
C    N    5
C    D    4
C    F    2
C    Z    5
C    L    5

cat D.關系圖.xlsx
Source    Target    Weight
D    H    3
D    J    4
D    K    4
D    C    4
D    B    6
D    A    6
D    F    6

2.

import  pandas as pd
import networkx as nx
 
from networkx.algorithms.approximation import  clique
import matplotlib.pyplot as plt
data1 = pd.read_excel("./數據集/3.4.簡單社交網絡/A關系圖.xlsx")
data2 = pd.read_excel("./數據集/3.4.簡單社交網絡/B關系圖.xlsx")
data3 = pd.read_excel("./數據集/3.4.簡單社交網絡/C關系圖.xlsx")
data4 = pd.read_excel("./數據集/3.4.簡單社交網絡/D關系圖.xlsx")
data = pd.concat([data1,data2,data3,data4])
 
print (data)
# from_pandas_edgelist 函數使用數據集創建邊和頂點
graph = nx.from_pandas_edgelist(data,‘Source‘,‘Target‘,edge_attr=[‘Weight‘])
#頂點
#print(graph.nodes())
#邊
#print(graph.edges())
#頂點權重
#print(graph.degree())
#創建圖像實例
plt.figure(figsize = (20,10))
#節點的顏色由節點的度決定
node_color = [graph.degree[v] for v in graph]
#邊的寬度由權重決定
edge_size = [0.2*graph[u][v][‘Weight‘] for u,v in graph.edges()]
pos = nx.spring_layout(graph)
#畫圖
nx.draw_networkx(graph,pos=pos,with_labels=False,node_color=node_color)
plt.show()




#社區探測
#help(clique)
klist = list(clique.clique_removal(graph))
print(len(klist))
plt.figure(figsize=(20,10))
nx.draw_networkx(graph,pos=pos,nodelist=klist[0],node_color= ‘r‘)
nx.draw_networkx(graph,pos=pos,node_list=klist[1],node_color=‘y‘)
plt.savefig("./a.jpg")

簡單的社交網絡