基于pagerank算法进行邮件PR分析.zip

# -*- coding: utf-8 -*- # 用PageRank挖掘希拉里邮件中的重要任务关系 import pandas as pd import networkx as nx import numpy as np from collections import defaultdict import matplotlib.pyplot as plt # 数据加载 emails = pd.read_csv("./input/Emails.csv") # 读取别名文件 file = pd.read_csv("./input/Aliases.csv") aliases = {} for index, row in file.iterrows(): aliases[row['Alias']] = row['PersonId'] # 读取人名文件 file = pd.read_csv("./input/Persons.csv") persons = {} for index, row in file.iterrows(): persons[row['Id']] = row['Name'] # 针对别名进行转换 def unify_name(name): # 姓名统一小写 name = str(name).lower() # 去掉, 和@后面的内容 name = name.replace(",","").split("@")[0] # 别名转换 if name in aliases.keys(): return persons[aliases[name]] return name # 画网络图 def show_graph(graph): # 使用Spring Layout布局，类似中心放射状 positions=nx.spring_layout(graph) # 设置网络图中的节点大小，大小与pagerank值相关，因为pagerank值很小所以需要*20000 nodesize = [x['pagerank']*20000 for v,x in graph.nodes(data=True)] # 设置网络图中的边长度 edgesize = [np.sqrt(e[2]['weight']) for e in graph.edges(data=True)] # 绘制节点 nx.draw_networkx_nodes(graph, positions, node_size=nodesize, alpha=0.4) # 绘制边 nx.draw_networkx_edges(graph, positions, edge_size=edgesize, alpha=0.2) # 绘制节点的label nx.draw_networkx_labels(graph, positions, font_size=10) # 输出希拉里邮件中的所有人物关系图 plt.show() # 将寄件人和收件人的姓名进行规范化 emails.MetadataFrom = emails.MetadataFrom.apply(unify_name) emails.MetadataTo = emails.MetadataTo.apply(unify_name) # 设置遍的权重等于发邮件的次数 edges_weights_temp = defaultdict(list) for row in zip(emails.MetadataFrom, emails.MetadataTo, emails.RawText): temp = (row[0], row[1]) if temp not in edges_weights_temp: edges_weights_temp[temp] = 1 else: edges_weights_temp[temp] = edges_weights_temp[temp] + 1 # 转化格式 (from, to), weight => from, to, weight edges_weights = [(key[0], key[1], val) for key, val in edges_weights_temp.items()] # 创建一个有向图 graph = nx.DiGraph() # 设置有向图中的路径及权重(from, to, weight) graph.add_weighted_edges_from(edges_weights) # 计算每个节点（人）的PR值，并作为节点的pagerank属性 pagerank = nx.pagerank(graph) # 获取每个节点的pagerank数值 pagerank_list = {node: rank for node, rank in pagerank.items()} # 将pagerank数值作为节点的属性 nx.set_node_attributes(graph, name = 'pagerank', values=pagerank_list) # 画网络图 show_graph(graph) # 将完整的图谱进行精简 # 设置PR值的阈值，筛选大于阈值的重要核心节点 pagerank_threshold = 0.005 # 复制一份计算好的网络图 small_graph = graph.copy() # 剪掉PR值小于pagerank_threshold的节点 for n, p_rank in graph.nodes(data=True): if p_rank['pagerank'] < pagerank_threshold: small_graph.remove_node(n) # 画网络图 show_graph(small_graph)