co-citation_network 共引文献网络分析.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:25:29.676918Z",
     "start_time": "2020-08-28T09:25:14.791326Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key \"text.kerning_factor\" on line 4 in\n",
      "E:\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test_patch.mplstyle.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "import networkx as nx#复杂网络分析库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:26:02.916567Z",
     "start_time": "2020-08-28T09:26:02.472502Z"
    }
   },
   "outputs": [],
   "source": [
    "xls = pd.ExcelFile(r'G:/CDO/知网研学/CNKI-analysis-master/dependence/知网数据.xls')#读取xls表格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:26:50.066528Z",
     "start_time": "2020-08-28T09:26:49.975521Z"
    }
   },
   "outputs": [],
   "source": [
    "readf = pd.read_excel(xls,'Sheet1')#读取第一个表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:28:29.782385Z",
     "start_time": "2020-08-28T09:28:29.703473Z"
    }
   },
   "outputs": [],
   "source": [
    "frame = readf[readf['共引文献'].notnull()]#如果关键词那一列非空，读取所有数据 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:28:38.469002Z",
     "start_time": "2020-08-28T09:28:38.459043Z"
    }
   },
   "outputs": [],
   "source": [
    "keywords = []#关键词列表，里面记录了所有的关键词，没有重复\n",
    "for keyword in frame['共引文献']:#分隔关键词，并加入到列表中，去重\n",
    "    if ',' in keyword:\n",
    "        temp = keyword.split(',')\n",
    "        for x in temp:\n",
    "            if x not in keywords:\n",
    "                keywords.append(x)\n",
    "    elif ';' in keyword:\n",
    "        temp = keyword.split(';')\n",
    "        for x in temp:\n",
    "            if x not in keywords:\n",
    "                keywords.append(x)\n",
    "    else:\n",
    "        if keyword not in keywords:\n",
    "            keywords.append(keyword)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:28:46.311915Z",
     "start_time": "2020-08-28T09:28:46.219390Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(index=frame['序号'],columns=keywords)    #建立以标题为行，关键词为列的DataFrame矩阵\n",
    "df.index.name='序号'\n",
    "df.columns.name='共引文献' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:28:52.772301Z",
     "start_time": "2020-08-28T09:28:50.077352Z"
    }
   },
   "outputs": [],
   "source": [
    "for row in frame['序号']:#将这一篇文献所拥有的关键词在矩阵中标记为1\n",
    "    # print(row)\n",
    "    for keyword in df.columns:\n",
    "        if keyword in frame.loc[row]['共引文献']:\n",
    "            #print(keyword)\n",
    "            df.loc[row][keyword] = 1  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:29:05.124365Z",
     "start_time": "2020-08-28T09:29:05.032116Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df.fillna(0)#填充空值 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:29:12.970231Z",
     "start_time": "2020-08-28T09:29:12.903769Z"
    }
   },
   "outputs": [],
   "source": [
    "data = df.values.T.dot(df.values)\n",
    "#建立关键词之间的相关性，边的长度为相关性，在这里是将两个df点乘，df.values是按行读取值\n",
    "#print(df.values)#这个时候data其实是关联度 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:29:18.113854Z",
     "start_time": "2020-08-28T09:29:17.936400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 ['基于区块链技术视角下未来财务会计发展方向研究'  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                               30   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                              30   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                           30   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                                 30   \n",
      " '我国互联网金融监管理论研究及现状分析'                                   30   \n",
      "...                                                    ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                               0   \n",
      " '台湾智慧社区发展的最新实践'                                         0   \n",
      " '面向智慧城市的社区信息化管理研究'                                      0   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                          0   \n",
      " '中国城市社区管理创新模式分析']                                       0   \n",
      "\n",
      "                                  '基于区块链理念探索“一带一路”区域的新零售模式'  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                                30   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                               60   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                            30   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                                  30   \n",
      " '我国互联网金融监管理论研究及现状分析'                                    30   \n",
      "...                                                     ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                                0   \n",
      " '台湾智慧社区发展的最新实践'                                          0   \n",
      " '面向智慧城市的社区信息化管理研究'                                       0   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                           0   \n",
      " '中国城市社区管理创新模式分析']                                        0   \n",
      "\n",
      "                                  '收集个人信息的电子合同中格式条款与强制缔约的问题研究'  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                                   30   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                                  30   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                               30   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                                     30   \n",
      " '我国互联网金融监管理论研究及现状分析'                                       30   \n",
      "...                                                        ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                                   0   \n",
      " '台湾智慧社区发展的最新实践'                                             0   \n",
      " '面向智慧城市的社区信息化管理研究'                                          0   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                              0   \n",
      " '中国城市社区管理创新模式分析']                                           0   \n",
      "\n",
      "                                  '区块链智联世界:去中心化的信用中介2.0'  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                             30   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                            30   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                         30   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                               90   \n",
      " '我国互联网金融监管理论研究及现状分析'                                 30   \n",
      "...                                                  ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                             0   \n",
      " '台湾智慧社区发展的最新实践'                                       0   \n",
      " '面向智慧城市的社区信息化管理研究'                                    0   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                        0   \n",
      " '中国城市社区管理创新模式分析']                                     0   \n",
      "\n",
      "                                  '我国互联网金融监管理论研究及现状分析'  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                           30   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                          30   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                       30   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                             30   \n",
      " '我国互联网金融监管理论研究及现状分析'                               30   \n",
      "...                                                ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                           0   \n",
      " '台湾智慧社区发展的最新实践'                                     0   \n",
      " '面向智慧城市的社区信息化管理研究'                                  0   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                      0   \n",
      " '中国城市社区管理创新模式分析']                                   0   \n",
      "\n",
      "                                  '区块链技术下价值互联网对传统金融机构的影响'  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                              30   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                             30   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                          30   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                                90   \n",
      " '我国互联网金融监管理论研究及现状分析'                                  30   \n",
      "...                                                   ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                              0   \n",
      " '台湾智慧社区发展的最新实践'                                        0   \n",
      " '面向智慧城市的社区信息化管理研究'                                     0   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                         0   \n",
      " '中国城市社区管理创新模式分析']                                      0   \n",
      "\n",
      "                                  '基于区块链技术的混凝土泵车泵送方量数据存储研究'  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                                30   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                               30   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                            30   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                                  90   \n",
      " '我国互联网金融监管理论研究及现状分析'                                    30   \n",
      "...                                                     ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                                0   \n",
      " '台湾智慧社区发展的最新实践'                                          0   \n",
      " '面向智慧城市的社区信息化管理研究'                                       0   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                           0   \n",
      " '中国城市社区管理创新模式分析']                                        0   \n",
      "\n",
      "                                  '论互联网金融消费者权益保护的有效路径'   '浅谈区块链金融的机遇与挑战'  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                           30                30   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                          30                30   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                       30                30   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                             30                90   \n",
      " '我国互联网金融监管理论研究及现状分析'                               30                30   \n",
      "...                                                ...               ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                           0                 0   \n",
      " '台湾智慧社区发展的最新实践'                                     0                 0   \n",
      " '面向智慧城市的社区信息化管理研究'                                  0                 0   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                      0                 0   \n",
      " '中国城市社区管理创新模式分析']                                   0                 0   \n",
      "\n",
      "                                  '区块链对互联网金融行业的变革展望'  ...  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                         30  ...   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                        30  ...   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                     30  ...   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                           90  ...   \n",
      " '我国互联网金融监管理论研究及现状分析'                             30  ...   \n",
      "...                                              ...  ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                         0  ...   \n",
      " '台湾智慧社区发展的最新实践'                                   0  ...   \n",
      " '面向智慧城市的社区信息化管理研究'                                0  ...   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                    0  ...   \n",
      " '中国城市社区管理创新模式分析']                                 0  ...   \n",
      "\n",
      "                                 ['基于GIS的街道信息管理系统设计与实现'   '信息平台建设助力街道安全监督'  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                             0                  0   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                            0                  0   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                         0                  0   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                               0                  0   \n",
      " '我国互联网金融监管理论研究及现状分析'                                 0                  0   \n",
      "...                                                 ...                ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                           30                 30   \n",
      " '台湾智慧社区发展的最新实践'                                     30                 30   \n",
      " '面向智慧城市的社区信息化管理研究'                                  30                 30   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                      30                 30   \n",
      " '中国城市社区管理创新模式分析']                                   30                 30   \n",
      "\n",
      "                                  '智慧社区背景下社区民警服务群众创新研究'   '云平台下智慧家庭系统的具体实现'  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                             0                   0   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                            0                   0   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                         0                   0   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                               0                   0   \n",
      " '我国互联网金融监管理论研究及现状分析'                                 0                   0   \n",
      "...                                                 ...                 ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                           30                  30   \n",
      " '台湾智慧社区发展的最新实践'                                     30                  30   \n",
      " '面向智慧城市的社区信息化管理研究'                                  30                  30   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                      30                  30   \n",
      " '中国城市社区管理创新模式分析']                                   30                  30   \n",
      "\n",
      "                                  '地铁上盖的智慧社区规划与设计'  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                        0   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                       0   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                    0   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                          0   \n",
      " '我国互联网金融监管理论研究及现状分析'                            0   \n",
      "...                                            ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                      30   \n",
      " '台湾智慧社区发展的最新实践'                                30   \n",
      " '面向智慧城市的社区信息化管理研究'                             30   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                 30   \n",
      " '中国城市社区管理创新模式分析']                              30   \n",
      "\n",
      "                                  '基于结构方程模型的智慧社区购买意愿影响因素研究'   '台湾智慧社区发展的最新实践'  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                                 0                 0   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                                0                 0   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                             0                 0   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                                   0                 0   \n",
      " '我国互联网金融监管理论研究及现状分析'                                     0                 0   \n",
      "...                                                     ...               ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                               30                30   \n",
      " '台湾智慧社区发展的最新实践'                                         30                30   \n",
      " '面向智慧城市的社区信息化管理研究'                                      30                30   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                          30                30   \n",
      " '中国城市社区管理创新模式分析']                                       30                30   \n",
      "\n",
      "                                  '面向智慧城市的社区信息化管理研究'  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                          0   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                         0   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                      0   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                            0   \n",
      " '我国互联网金融监管理论研究及现状分析'                              0   \n",
      "...                                              ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                        30   \n",
      " '台湾智慧社区发展的最新实践'                                  30   \n",
      " '面向智慧城市的社区信息化管理研究'                               30   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                   30   \n",
      " '中国城市社区管理创新模式分析']                                30   \n",
      "\n",
      "                                  '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'  \\\n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                                      0   \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                                     0   \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                                  0   \n",
      " '区块链智联世界:去中心化的信用中介2.0'                                        0   \n",
      " '我国互联网金融监管理论研究及现状分析'                                          0   \n",
      "...                                                          ...   \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                                    30   \n",
      " '台湾智慧社区发展的最新实践'                                              30   \n",
      " '面向智慧城市的社区信息化管理研究'                                           30   \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                               30   \n",
      " '中国城市社区管理创新模式分析']                                            30   \n",
      "\n",
      "                                  '中国城市社区管理创新模式分析']  \n",
      "['基于区块链技术视角下未来财务会计发展方向研究'                         0  \n",
      " '基于区块链理念探索“一带一路”区域的新零售模式'                        0  \n",
      " '收集个人信息的电子合同中格式条款与强制缔约的问题研究'                     0  \n",
      " '区块链智联世界:去中心化的信用中介2.0'                           0  \n",
      " '我国互联网金融监管理论研究及现状分析'                             0  \n",
      "...                                             ...  \n",
      " '基于结构方程模型的智慧社区购买意愿影响因素研究'                       30  \n",
      " '台湾智慧社区发展的最新实践'                                 30  \n",
      " '面向智慧城市的社区信息化管理研究'                              30  \n",
      " '智慧社区运营管理体系及平台构建研究——基于利益相关者视角'                  30  \n",
      " '中国城市社区管理创新模式分析']                               30  \n",
      "\n",
      "[402 rows x 402 columns]\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.DataFrame(data = data,index=keywords,columns=keywords)#建立关键词之间的相关性矩阵\n",
    "\n",
    "value = lambda x : x * 30 if x > 0 else 0\n",
    "df2 = df2.applymap(value)\n",
    "# for i in range(0, len(df2.index)):\n",
    "#     for j in range(0, len(df2.columns)):\n",
    "#         if df2.iloc[i][j] < 4:#这里设置阈值\n",
    "#             df2.iloc[i][j] = 0\n",
    "print(df2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:29:32.301915Z",
     "start_time": "2020-08-28T09:29:32.241952Z"
    }
   },
   "outputs": [],
   "source": [
    "net = nx.Graph(df2)#创建无向图，以关键词为节点，相关性为边 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:29:36.499823Z",
     "start_time": "2020-08-28T09:29:35.681063Z"
    }
   },
   "outputs": [],
   "source": [
    "de=dict(net.degree())#建立字典，关键字为索引，度（关联情况）为值\n",
    "pos = nx.spring_layout(net)#四种建图模式，spectral,shell,circular,spring，spring是可以看的了\n",
    "keywords = [i for i in keywords]#有边的关键词  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:29:42.253389Z",
     "start_time": "2020-08-28T09:29:42.244421Z"
    }
   },
   "outputs": [],
   "source": [
    "array = np.zeros(len(keywords))#建立以度为值的一维矩阵\n",
    "j = 0#记录值的数量\n",
    "for i in de.keys():\n",
    "    array[j] = de[i]\n",
    "    j+=1\n",
    "arg = np.argsort(-np.array(array))\n",
    "labels = {}#记录关键词\n",
    "for index in range(0, len(keywords)):\n",
    "    labels[keywords[arg[index]]] = keywords[arg[index]]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:29:45.950921Z",
     "start_time": "2020-08-28T09:29:45.944939Z"
    }
   },
   "outputs": [],
   "source": [
    "de2 = [de[v]*10 for v in sorted(de.keys(), reverse=False)]#应该是节点的大小，尺寸调整合适  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T09:30:02.101387Z",
     "start_time": "2020-08-28T09:29:59.704108Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3600x3600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(50, 50))\n",
    "#nx.draw_networkx_labels(net,pos,labels, font_size=40,font_color='black',font_family ='YouYuan')\n",
    "nx.draw_networkx(net, pos, node_size=de2, with_labels = False, node_color='#A52A2A', linewidths=None, width=2.0, edge_color ='#858585')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T10:57:51.807899Z",
     "start_time": "2020-08-28T10:57:51.505346Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import networkx as nx#复杂网络分析库\n",
    "import network\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T13:33:29.536883Z",
     "start_time": "2020-08-28T13:33:29.008066Z"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'network' has no attribute 'seperate'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-d64cb3a2db62>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mframe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreadf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mreadf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'共引文献'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnotnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;31m#如果关键词那一列非空，读取所有数据\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mkeywords\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnetwork\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseperate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'共引文献'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m' '\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m';'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;31m#关键词列表，里面记录了所有的关键词，没有重复\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# for keyword in frame['共引文献']:#分隔关键词，并加入到列表中，去重\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'network' has no attribute 'seperate'"
     ]
    }
   ],
   "source": [
    "path = os.path.abspath('G:/CDO/知网研学/CNKI-analysis-master/dependence/')\n",
    "xls = pd.ExcelFile(path+'/知网数据.xls')#读取xls表格\n",
    "readf = pd.read_excel(xls,'Sheet1')#读取第一个表\n",
    "frame = readf[readf['共引文献'].notnull()]#如果关键词那一列非空，读取所有数据\n",
    "\n",
    "keywords = network.seperate(frame, '共引文献', ' ', ';')\n",
    "#关键词列表，里面记录了所有的关键词，没有重复\n",
    "# for keyword in frame['共引文献']:#分隔关键词，并加入到列表中，去重\n",
    "#     if ',' in keyword:\n",
    "#         temp = keyword.split(',')\n",
    "#         for x in temp:\n",
    "#             if x not in keywords:\n",
    "#                 keywords.append(x)\n",
    "#     elif ';' in keyword:\n",
    "#         temp = keyword.split(';')\n",
    "#         for x in temp:\n",
    "#             if x not in keywords:\n",
    "#                 keywords.append(x)\n",
    "#     else:\n",
    "#         if keyword not in keywords:\n",
    "#             keywords.append(keyword)\n",
    "\n",
    "df = pd.DataFrame(index=frame['序号'],columns=keywords)    #建立以标题为行，关键词为列的DataFrame矩阵\n",
    "df.index.name='序号'\n",
    "df.columns.name='共引文献'\n",
    "#\n",
    "# #将这一篇文献所拥有的关键词在矩阵中标记为1\n",
    "# for row in frame['序号']:\n",
    "#     for keyword in df.columns:\n",
    "#         if keyword in frame.loc[row]['共引文献']:\n",
    "#             df.loc[row][keyword] = 1\n",
    "# df = df.fillna(0)#填充空值\n",
    "\n",
    "df = network.fill(frame, '序号', '共引文献', df)\n",
    "\n",
    "#df为存在矩阵，dataframe类型\n",
    "#data为关联度，矩阵类型\n",
    "#df2位关联度矩阵，dataframe类型\n",
    "\n",
    "data = df.values.T.dot(df.values)#建立关键词之间的相关性，边的长度为相关性，在这里是将两个df点乘，df.values是按行读取值\n",
    "df2 = pd.DataFrame(data = data,index=keywords,columns=keywords)#建立关键词之间的相关性矩阵，以关联度作为值传入\n",
    "\n",
    "#设置阈值\n",
    "value = lambda x : x * 30 if x > 0 else 0\n",
    "df2 = df2.applymap(value)\n",
    "\n",
    "net = nx.Graph(df2)#创建无向图，以关键词为节点，相关性为边\n",
    "\n",
    "dele, net = network.remove(keywords, net)\n",
    "\n",
    "de=dict(net.degree())#建立字典，关键字为索引，度（关联情况）为值\n",
    "pos = nx.spring_layout(net)#四种建图模式，spectral,shell,circular,spring，spring是可以看的了\n",
    "\n",
    "array = np.zeros(len(keywords))#建立以度为值的一维矩阵\n",
    "arg = np.argsort(-np.array(array))\n",
    "labels = {}#记录关键词\n",
    "for index in range(0, len(keywords)):\n",
    "    labels[keywords[arg[index]]] = keywords[arg[index]]\n",
    "\n",
    "de2 = [de[v]*10 for v in sorted(de.keys(), reverse=False)]#应该是节点的大小，尺寸调整合适\n",
    "\n",
    "plt.figure(figsize=(50, 50))\n",
    "nx.draw_networkx_labels(net,pos,labels, font_size=40,font_color='black',font_family ='YouYuan')#写标记\n",
    "nx.draw_networkx(net, pos, node_size=de2, with_labels = False, node_color='#A52A2A', linewidths=None, width=2.0, edge_color ='#858585')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T13:33:49.010126Z",
     "start_time": "2020-08-28T13:33:48.976826Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T13:34:40.827547Z",
     "start_time": "2020-08-28T13:34:40.527682Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>序号</th>\n",
       "      <th>二级参考</th>\n",
       "      <th>作者</th>\n",
       "      <th>共引文献</th>\n",
       "      <th>关键词</th>\n",
       "      <th>参考文献</th>\n",
       "      <th>年份</th>\n",
       "      <th>机构</th>\n",
       "      <th>相似文献</th>\n",
       "      <th>相关作者</th>\n",
       "      <th>相关机构</th>\n",
       "      <th>题目+引用</th>\n",
       "      <th>题目</th>\n",
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       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>《暨南大学》</td>\n",
       "      <td>['基于区块链机制的云计算环境下服务组合策略的研究', '浅谈区块链的设计与未来', '基于...</td>\n",
       "      <td>['徐蕾', '陈何清', '安庆文', '王成', '滕达', '张鹏', '谢雨', '...</td>\n",
       "      <td>['南京大学', '浙江大学', '山东大学', '西南科技大学', '首都经济贸易大学',...</td>\n",
       "      <td>金融服务区块链应用场景及其案例分析--《暨南大学》2018年硕士论文</td>\n",
       "      <td>金融服务区块链应用场景及其案例分析</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>['去中心化金融与区块链', '区块链带来金融与法律优化', '数字货币技术实现框架构想',...</td>\n",
       "      <td>['高昀']</td>\n",
       "      <td>['基于区块链技术视角下未来财务会计发展方向研究', '基于区块链理念探索“一带一路”区域的...</td>\n",
       "      <td>区块链 金融企业 监管</td>\n",
       "      <td>['The DAO被攻击事件分析与思考', '去中心化金融与区块链', '从R3区块链联盟看...</td>\n",
       "      <td>2017年</td>\n",
       "      <td>《兰州财经大学》</td>\n",
       "      <td>['于家堡金融区引援', '于家堡金融区引资超500亿', '西安金融商务区获评“最具投资价...</td>\n",
       "      <td>['徐蕾', '陈何清', '安庆文', '庹小忠', '滕达', '谢雨', '夏新岳',...</td>\n",
       "      <td>['上海国家会计学院', '西南石油大学', '山东大学', '东华大学', '西南科技大学...</td>\n",
       "      <td>我国金融区块链应用的法律问题研究--《兰州财经大学》2017年硕士论文</td>\n",
       "      <td>我国金融区块链应用的法律问题研究</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['王元地', '李粒', '胡谍']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>区块链 交易流程 基础架构 优势及问题 理论来源 技术支撑 应用及发展</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018年03期</td>\n",
       "      <td>《中国矿业大学学报(社会科学版)》</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>区块链研究综述--《中国矿业大学学报(社会科学版)》2018年03期\\r\\n</td>\n",
       "      <td>区块链研究综述</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['宫晓林', '杨望', '曲双石']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>区块链 信息不对称 比特币 金融科技</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017年02期</td>\n",
       "      <td>《国际金融》</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>区块链的技术原理及其在金融领域的应用--《国际金融》2017年02期\\r\\n</td>\n",
       "      <td>区块链的技术原理及其在金融领域的应用</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['中国人民银行合肥中心支行科技处课题组', '谢铉洋']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>区块链 数字货币 去中心化</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017年01期</td>\n",
       "      <td>《金融纵横》</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>区块链结构、参与主体及应用展望--《金融纵横》2017年01期\\r\\n</td>\n",
       "      <td>区块链结构、参与主体及应用展望</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   序号                                               二级参考  \\\n",
       "0   0                                                NaN   \n",
       "1   1  ['去中心化金融与区块链', '区块链带来金融与法律优化', '数字货币技术实现框架构想',...   \n",
       "2   2                                                NaN   \n",
       "3   3                                                NaN   \n",
       "4   4                                                NaN   \n",
       "\n",
       "                              作者  \\\n",
       "0                        ['朱文晶']   \n",
       "1                         ['高昀']   \n",
       "2            ['王元地', '李粒', '胡谍']   \n",
       "3           ['宫晓林', '杨望', '曲双石']   \n",
       "4  ['中国人民银行合肥中心支行科技处课题组', '谢铉洋']   \n",
       "\n",
       "                                                共引文献  \\\n",
       "0                                                NaN   \n",
       "1  ['基于区块链技术视角下未来财务会计发展方向研究', '基于区块链理念探索“一带一路”区域的...   \n",
       "2                                                NaN   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "\n",
       "                                   关键词  \\\n",
       "0                        区块链 金融服务 应用场景   \n",
       "1                          区块链 金融企业 监管   \n",
       "2  区块链 交易流程 基础架构 优势及问题 理论来源 技术支撑 应用及发展   \n",
       "3                   区块链 信息不对称 比特币 金融科技   \n",
       "4                        区块链 数字货币 去中心化   \n",
       "\n",
       "                                                参考文献        年份  \\\n",
       "0                                                NaN     2018年   \n",
       "1  ['The DAO被攻击事件分析与思考', '去中心化金融与区块链', '从R3区块链联盟看...     2017年   \n",
       "2                                                NaN  2018年03期   \n",
       "3                                                NaN  2017年02期   \n",
       "4                                                NaN  2017年01期   \n",
       "\n",
       "                  机构                                               相似文献  \\\n",
       "0             《暨南大学》  ['基于区块链机制的云计算环境下服务组合策略的研究', '浅谈区块链的设计与未来', '基于...   \n",
       "1           《兰州财经大学》  ['于家堡金融区引援', '于家堡金融区引资超500亿', '西安金融商务区获评“最具投资价...   \n",
       "2  《中国矿业大学学报(社会科学版)》                                                NaN   \n",
       "3             《国际金融》                                                NaN   \n",
       "4             《金融纵横》                                                NaN   \n",
       "\n",
       "                                                相关作者  \\\n",
       "0  ['徐蕾', '陈何清', '安庆文', '王成', '滕达', '张鹏', '谢雨', '...   \n",
       "1  ['徐蕾', '陈何清', '安庆文', '庹小忠', '滕达', '谢雨', '夏新岳',...   \n",
       "2                                                NaN   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "\n",
       "                                                相关机构  \\\n",
       "0  ['南京大学', '浙江大学', '山东大学', '西南科技大学', '首都经济贸易大学',...   \n",
       "1  ['上海国家会计学院', '西南石油大学', '山东大学', '东华大学', '西南科技大学...   \n",
       "2                                                NaN   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "\n",
       "                                            题目+引用                  题目  \n",
       "0              金融服务区块链应用场景及其案例分析--《暨南大学》2018年硕士论文   金融服务区块链应用场景及其案例分析  \n",
       "1             我国金融区块链应用的法律问题研究--《兰州财经大学》2017年硕士论文    我国金融区块链应用的法律问题研究  \n",
       "2  区块链研究综述--《中国矿业大学学报(社会科学版)》2018年03期\\r\\n                     区块链研究综述  \n",
       "3  区块链的技术原理及其在金融领域的应用--《国际金融》2017年02期\\r\\n          区块链的技术原理及其在金融领域的应用  \n",
       "4     区块链结构、参与主体及应用展望--《金融纵横》2017年01期\\r\\n             区块链结构、参与主体及应用展望  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = os.path.abspath('G:/CDO/知网研学/CNKI-analysis-master/dependence/')\n",
    "xlsx = pd.ExcelFile(path+'/知网数据.xls') #读取数据文件\n",
    "readf = pd.read_excel(xlsx,'Sheet1')\n",
    "readf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T13:35:07.440753Z",
     "start_time": "2020-08-28T13:35:07.135612Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "#数据进行预处理，将读入的作者以及相关作者信息转换为列表形式\n",
    "for i in range(len(readf['作者'])):\n",
    "    if readf['作者'][i] != '[]':\n",
    "        readf['作者'][i] = eval(readf['作者'][i])\n",
    "    else:\n",
    "        readf['作者'][i] = np.nan\n",
    "for j in range(len(readf['相关作者'])):\n",
    "    if readf['相关作者'][j] is not np.nan:\n",
    "        readf['相关作者'][j] = eval(readf['相关作者'][j]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T13:35:30.332769Z",
     "start_time": "2020-08-28T13:35:30.322818Z"
    }
   },
   "outputs": [],
   "source": [
    "#数据预处理，将作者为空的数据去除\n",
    "frame = readf[readf['作者'].notnull()]\n",
    "frame.index = frame['题目'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T14:04:12.722694Z",
     "start_time": "2020-08-28T14:04:11.340716Z"
    }
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "nan",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2645\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2646\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine._get_loc_duplicates\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine._maybe_get_bool_indexer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: nan",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-8ebd8ea85046>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'作者'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtitle\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mframe\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'题目'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mframe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'作者'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m         \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mframe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'相关作者'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1765\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1766\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1767\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1768\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1769\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1962\u001b[0m         \u001b[1;31m# fall thru to straight lookup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1963\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1964\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1965\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1966\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m    622\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"no slices here, handle elsewhere\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    623\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 624\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    625\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    626\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mxs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m   3535\u001b[0m             \u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc_level\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdrop_level\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3536\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3537\u001b[1;33m             \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3538\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3539\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2646\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2648\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2650\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine._get_loc_duplicates\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine._maybe_get_bool_indexer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: nan"
     ]
    }
   ],
   "source": [
    "#获取作者以及相关作者，将其整合到一个列表中\n",
    "all_authors = []\n",
    "for authors in frame['作者']:\n",
    "    for author in authors:\n",
    "        if author not in all_authors:\n",
    "            all_authors.append(author)\n",
    "for r_authors in frame['相关作者']:\n",
    "    if r_authors is not np.nan:\n",
    "        for r_author in r_authors:\n",
    "            if r_author not in all_authors:\n",
    "                all_authors.append(r_author) \n",
    "                \n",
    "#构建出现矩阵\n",
    "df = pd.DataFrame(index=frame['题目'],columns=all_authors)\n",
    "df.index.name='题目'\n",
    "df.columns.name='作者'\n",
    "for title in frame['题目']:\n",
    "    for i in frame.loc[title]['作者']:\n",
    "        df.loc[title,i] = 1\n",
    "    if frame.loc[title]['相关作者'] is not np.nan:\n",
    "        for j in frame.loc[title]['相关作者']:\n",
    "            df.loc[title,j] = 1\n",
    "df=df.fillna(0) \n",
    "#将出现矩阵转换为共现矩阵\n",
    "data = df.values.T.dot(df.values)\n",
    "df2 = pd.DataFrame(data = data,index=all_authors,columns=all_authors)\n",
    "\n",
    "#设置阀门，排除关联度小的点\n",
    "valve = lambda x : x if x > 32 else 0\n",
    "df2 = df2.applymap(valve)\n",
    "\n",
    "#构建共现网络\n",
    "net = nx.Graph(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T14:04:41.565379Z",
     "start_time": "2020-08-28T14:04:41.332320Z"
    }
   },
   "outputs": [
    {
     "ename": "NodeNotFound",
     "evalue": "Either source 朱文晶 or target 高昀 is not in G",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNodeNotFound\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-c5823f82c39b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mdele\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_authors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcheck\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_authors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnet\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mall_authors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdele\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m             \u001b[0mdele\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_authors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-28-c5823f82c39b>\u001b[0m in \u001b[0;36mcheck\u001b[1;34m(x, net)\u001b[0m\n\u001b[0;32m      5\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_authors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_authors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhas_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mall_authors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda3\\lib\\site-packages\\networkx\\algorithms\\shortest_paths\\generic.py\u001b[0m in \u001b[0;36mhas_path\u001b[1;34m(G, source, target)\u001b[0m\n\u001b[0;32m     38\u001b[0m     \"\"\"\n\u001b[0;32m     39\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 40\u001b[1;33m         \u001b[0mnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshortest_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mG\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     41\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNetworkXNoPath\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda3\\lib\\site-packages\\networkx\\algorithms\\shortest_paths\\generic.py\u001b[0m in \u001b[0;36mshortest_path\u001b[1;34m(G, source, target, weight, method)\u001b[0m\n\u001b[0;32m    168\u001b[0m             \u001b[1;31m# Find shortest source-target path.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    169\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'unweighted'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 170\u001b[1;33m                 \u001b[0mpaths\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbidirectional_shortest_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mG\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    171\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'dijkstra'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    172\u001b[0m                 \u001b[0mpaths\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdijkstra_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mG\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda3\\lib\\site-packages\\networkx\\algorithms\\shortest_paths\\unweighted.py\u001b[0m in \u001b[0;36mbidirectional_shortest_path\u001b[1;34m(G, source, target)\u001b[0m\n\u001b[0;32m    221\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0msource\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mG\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mtarget\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mG\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    222\u001b[0m         \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Either source {} or target {} is not in G'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 223\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNodeNotFound\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    224\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    225\u001b[0m     \u001b[1;31m# call helper to do the real work\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNodeNotFound\u001b[0m: Either source 朱文晶 or target 高昀 is not in G"
     ]
    }
   ],
   "source": [
    "#过滤关联度为0的节点\n",
    "def check(x,net):\n",
    "    for i in range(0,all_authors.index(x)):\n",
    "        if nx.has_path(net,x,all_authors[i]):\n",
    "            return True\n",
    "    for j in range(all_authors.index(x)+1,len(all_authors)):\n",
    "        if nx.has_path(net,x,all_authors[j]):\n",
    "            return True\n",
    "    return False\n",
    "dele=[]\n",
    "for i in range(len(all_authors)):\n",
    "    if not check(all_authors[i],net):\n",
    "        if all_authors[i] not in dele:\n",
    "            dele.append(all_authors[i])\n",
    "net.remove_nodes_from(dele)\n",
    "\n",
    "#设置每个节点的大小比例为它们度的大小比例，并且显示每个节点的标签\n",
    "de=dict(net.degree())\n",
    "pos = nx.spring_layout(net)\n",
    "all_authors = [i for i in all_authors if i not in dele]\n",
    "array = np.zeros(len(all_authors))\n",
    "j = 0\n",
    "for i in de.keys():\n",
    "    array[j] = de[i]\n",
    "    j+=1\n",
    "arg = np.argsort(-np.array(array))\n",
    "labels = {}\n",
    "for index in range(len(all_authors)):\n",
    "    labels[all_authors[arg[index]]] = all_authors[arg[index]]\n",
    "de2 = [de[v]*20 for v in sorted(de.keys(), reverse=False)]\n",
    "\n",
    "#对网路进行可视化\n",
    "plt.figure(figsize=(50, 50))\n",
    "nx.draw_networkx_labels(net,pos,labels, font_size=40,font_color='black',font_family ='YouYuan')\n",
    "nx.draw_networkx(net, pos, node_size=de2, with_labels = False, node_color='#A52A2A', linewidths=None, width=1.0, edge_color ='#858585')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T14:06:52.351034Z",
     "start_time": "2020-08-28T14:06:51.967110Z"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'network' has no attribute 'seperate'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-27f7505e8016>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mframe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreadf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mreadf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'关键词'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnotnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;31m#如果关键词那一列非空，读取所有数据\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mkeywords\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnetwork\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseperate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'关键词'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m' '\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m';'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#关键词列表，里面记录了所有的关键词，没有重复\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: module 'network' has no attribute 'seperate'"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import networkx as nx#复杂网络分析库\n",
    "import network\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "path = os.path.abspath('G:/CDO/知网研学/CNKI-analysis-master/dependence/')\n",
    "xls = pd.ExcelFile(path+'/知网数据.xls')#读取xls表格\n",
    "readf = pd.read_excel(xls,'Sheet1')#读取第一个表\n",
    "frame = readf[readf['关键词'].notnull()]#如果关键词那一列非空，读取所有数据\n",
    "\n",
    "keywords = network.seperate(frame, '关键词', ' ', ';')#关键词列表，里面记录了所有的关键词，没有重复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T14:11:27.355784Z",
     "start_time": "2020-08-28T14:11:27.197753Z"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'network' has no attribute 'fill'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-30-e6f402cb43fa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;31m#\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;31m# df = df.fillna(0)#填充空值\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnetwork\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'序号'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'关键词'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#建立关键词之间的相关性，边的长度为相关性，在这里是将两个df点乘，df.values是按行读取值\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'network' has no attribute 'fill'"
     ]
    }
   ],
   "source": [
    "#建立以标题为行，关键词为列的DataFrame矩阵\n",
    "df = pd.DataFrame(index=frame['序号'],columns=keywords)\n",
    "df.index.name='序号'\n",
    "df.columns.name='关键词'\n",
    "\n",
    "# for row in frame['序号']:#将这一篇文献所拥有的关键词在矩阵中标记为1\n",
    "#     for keyword in df.columns:\n",
    "#         if keyword in frame.loc[row]['关键词']:\n",
    "#             #print(keyword)\n",
    "#             df.loc[row][keyword] = 1\n",
    "#\n",
    "# #df为存在矩阵，dataframe类型\n",
    "# #data为关联度，矩阵类型\n",
    "# #df2位关联度矩阵，dataframe类型\n",
    "#\n",
    "# df = df.fillna(0)#填充空值\n",
    "df = network.fill(frame, '序号', '关键词', df)\n",
    "\n",
    "data = df.values.T.dot(df.values)#建立关键词之间的相关性，边的长度为相关性，在这里是将两个df点乘，df.values是按行读取值\n",
    "\n",
    "df2 = pd.DataFrame(data = data,index=keywords,columns=keywords)#建立关键词之间的相关性矩阵\n",
    "\n",
    "#设置阈值\n",
    "valve = lambda x : x if x > 32 else 0\n",
    "df2 = df2.applymap(valve)\n",
    "\n",
    "net = nx.Graph(df2)#创建无向图，以关键词为节点，相关性为边\n",
    "\n",
    "# def check(x,net):\n",
    "#     for i in range(0,keywords.index(x)):\n",
    "#         if nx.has_path(net,x,keywords[i]):\n",
    "#             return True\n",
    "#     for j in range(keywords.index(x)+1,len(keywords)):\n",
    "#         if nx.has_path(net,x,keywords[j]):\n",
    "#             return True\n",
    "#     return False\n",
    "#\n",
    "# #去除无连接节点\n",
    "# dele=[]\n",
    "# for i in range(len(keywords)):\n",
    "#     if not check(keywords[i],net):\n",
    "#         if keywords[i] not in dele:\n",
    "#             dele.append(keywords[i])\n",
    "# net.remove_nodes_from(dele)\n",
    "dele, net = network.remove(keywords, net)\n",
    "\n",
    "de=dict(net.degree())#建立字典，关键字为索引，度（关联情况）为值\n",
    "pos = nx.spring_layout(net)#四种建图模式，spectral,shell,circular,spring，spring是可以看的了\n",
    "keywords = [i for i in keywords if i not in dele]#有边的关键词\n",
    "\n",
    "array = np.zeros(len(keywords))#建立以度为值的一维矩阵\n",
    "arg = np.argsort(-np.array(array))\n",
    "labels = {}#记录关键词\n",
    "for index in range(0, len(keywords)):\n",
    "    labels[keywords[arg[index]]] = keywords[arg[index]]\n",
    "\n",
    "de2 = [de[v]*60 for v in sorted(de.keys(), reverse=False)]#应该是节点的大小，尺寸调整合适\n",
    "\n",
    "plt.figure(figsize=(50, 50))\n",
    "nx.draw_networkx_labels(net,pos,labels, font_size=40,font_color='black',font_family ='YouYuan')\n",
    "nx.draw_networkx(net, pos, node_size=de2, with_labels = False, node_color='#A52A2A', linewidths=None, width=2.0, edge_color ='#858585')"
   ]
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