Task1/social_network_domain.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import string\n",
    "import numpy as np\n",
    "\n",
    "punc = string.punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_dic = {}\n",
    "word_dic = load_word2vec(dim)\n",
    "# set the dim\n",
    "dim = 100\n",
    "path = '/Users/zhengwei/Desktop/new_id.json'\n",
    "# '/Users/zhengwei/Desktop/lesson6/社交网络挖掘/dataset/dblp-ref-0.json'\n",
    "with open(path, 'r') as f:\n",
    "    line = f.readline()\n",
    "    temp_vec = np.zeros(dim)\n",
    "    while line:        \n",
    "        line = json.loads(line)\n",
    "        if 'abstract' in line:\n",
    "            abstract_ = line['abstract'].split(' ')\n",
    "            length = len(abstract_)\n",
    "            for item in abstract_:\n",
    "                if item == '':\n",
    "                    continue\n",
    "                if item[0] in punc:\n",
    "                    item = item[1:]\n",
    "#                 if item == '':\n",
    "#                     print(abstract_)\n",
    "                if item == '':\n",
    "                    continue\n",
    "                if item[-1] in punc:\n",
    "                    item = item[:-1]\n",
    "\n",
    "                if item in word_dic:\n",
    "                    temp_vec += np.array(word_dic[item], dtype=np.float64)\n",
    "                else:\n",
    "                    continue\n",
    "            result_dic[line['id']] = [temp_vec/length]\n",
    "        else:\n",
    "            result_dic[line['id']] = [temp_vec]\n",
    "        \n",
    "        temp_vec = np.zeros(dim)\n",
    "        title_ = line['title'].split(' ')\n",
    "        length = len(title_)\n",
    "        for item in title_:\n",
    "            if item == '':\n",
    "                    continue\n",
    "            if item[0] in punc:\n",
    "                item = item[1:]\n",
    "            if item == '':\n",
    "                    continue\n",
    "            if item[-1] in punc:\n",
    "                item = item[:-1]\n",
    "                \n",
    "            if item in word_dic:\n",
    "                temp_vec += np.array(word_dic[item], dtype=np.float64)\n",
    "            else:\n",
    "                continue\n",
    "        result_dic[line['id']].append(temp_vec/length)\n",
    "        \n",
    "        line = f.readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-4.77929687e-02,  2.45555391e-01,  2.34570080e-01,  4.96333547e-02,\n",
       "        5.78041875e-02, -4.76344531e-02, -8.38733594e-02,  9.17767559e-02,\n",
       "       -4.17209488e-01,  1.93618766e-01,  1.22915391e-01, -1.97329672e-01,\n",
       "        1.84115539e-01,  5.61010312e-03,  1.75752755e-01, -4.62975156e-02,\n",
       "        2.05226937e-01,  7.79395156e-02, -1.34265172e-01,  5.26235656e-02,\n",
       "        1.13390703e-01, -2.12066266e-01,  2.77592884e-01,  1.89173203e-01,\n",
       "        1.28171372e-01, -3.72696547e-01,  8.56345781e-02, -3.55042688e-01,\n",
       "       -2.17762844e-01,  1.22383750e-02,  3.00649375e-02,  1.98880953e-01,\n",
       "       -3.22155981e-01,  4.59024375e-02,  1.18302072e-01,  2.18529891e-01,\n",
       "        2.04114625e-01,  2.75034750e-01, -1.77718109e-01, -1.61010670e-01,\n",
       "       -3.31550528e-01, -3.57793734e-01, -6.22402500e-02, -8.19579625e-02,\n",
       "       -8.00023141e-02,  6.63968281e-02,  1.84564328e-01, -3.40089984e-01,\n",
       "       -2.12390291e-01, -5.96622594e-01,  2.87022969e-02,  2.47523453e-02,\n",
       "        9.73137031e-02,  9.00712578e-01, -2.21799500e-01, -1.84658373e+00,\n",
       "       -1.25868750e-03, -1.22369453e-01,  1.41382481e+00,  3.76650255e-01,\n",
       "       -2.39631470e-01,  5.08482500e-01, -1.96151744e-01,  3.58459234e-01,\n",
       "        4.68835772e-01, -4.30760937e-02,  6.79550266e-02,  6.80837969e-02,\n",
       "        3.22844341e-01, -7.21713750e-02, -1.84471063e-01, -2.11439062e-03,\n",
       "       -8.41908437e-02, -1.44900969e-01,  1.13871391e-01, -3.50267187e-03,\n",
       "       -7.92803594e-02,  3.22301977e-02, -7.90358336e-01, -2.40757833e-01,\n",
       "        5.12581264e-01,  1.76088187e-01, -4.12781172e-01,  1.73471290e-01,\n",
       "       -1.12603812e+00, -9.80925281e-02,  6.33745000e-02, -2.61483873e-01,\n",
       "       -3.48990781e-02, -2.53235391e-01, -8.29721328e-02, -2.26638125e-02,\n",
       "       -1.59352703e-01,  9.93027344e-02, -3.34438828e-01,  9.08039531e-02,\n",
       "       -3.25357792e-01, -3.52310203e-01,  6.26336250e-01,  1.89336531e-01])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_dic[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def load_word2vec(dim):\n",
    "    word_dic = {}\n",
    "    with open('/Users/zhengwei/Desktop/glove/glove.6B.'+ str(dim) + 'd.txt') as f:\n",
    "        line = f.readline()\n",
    "        while line:\n",
    "            line = line[:-1].split(' ')\n",
    "            word_dic[line[0]] = line[1:]\n",
    "            line = f.readline()\n",
    "    return word_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "def write_in_pickle(data, path):\n",
    "    output = open(path, 'wb')\n",
    "    pickle.dump(data, output)\n",
    "    output.close()\n",
    "\n",
    "def load_pickle(path):\n",
    "    pkl_file = open(path, 'rb')\n",
    "    data = pickle.load(pkl_file)\n",
    "    pkl_file.close()\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "write_in_pickle(result_dic, '/Users/zhengwei/Desktop/vector.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Task 2\n",
    "\n",
    "import json\n",
    "\n",
    "author_dic = {}\n",
    "with open('/Users/zhengwei/Desktop/lesson6/社交网络挖掘/dataset/dblp-ref-0.json', 'r') as f:\n",
    "    line = f.readline()\n",
    "    while line:  \n",
    "        line = json.loads(line)\n",
    "        author_list = line['authors']\n",
    "        for i in range(len(author_list)):\n",
    "            main_author = author_list[i]\n",
    "            if main_author not in author_dic:\n",
    "                author_dic[main_author] = {}\n",
    "            coo_author = author_list[:i] + author_list[i+1:]\n",
    "            for author in coo_author:\n",
    "                if author not in author_dic[main_author]:\n",
    "                    author_dic[main_author][author] = 1\n",
    "                else:\n",
    "                    author_dic[main_author][author] += 1\n",
    "        line = f.readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "write_in_pickle(author_dic, '/Users/zhengwei/Desktop/coo_author.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_pickle('/Users/zhengwei/Desktop/coo_author.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"-------------------------Extract small and valuable dataset--------------------\"\"\"\n",
    "import json\n",
    "import string\n",
    "import numpy as np\n",
    "punc = string.punctuation\n",
    "\n",
    "path = '/Users/zhengwei/Desktop/lesson6/社交网络挖掘/dataset/dblp-ref-all.json'\n",
    "result_dic = {}\n",
    "dim = 100\n",
    "word_dic = load_word2vec(dim)\n",
    "\n",
    "id_ref_num = {}\n",
    "with open(path, 'r') as f:\n",
    "    line = f.readline()\n",
    "    while line:        \n",
    "        line = json.loads(line)\n",
    "        if 'references' in line and len(line['references']) > 0:\n",
    "            for item in line['references']:\n",
    "                if item not in id_ref_num:\n",
    "                    id_ref_num[item] = 1\n",
    "                else:\n",
    "                    id_ref_num[item] += 1\n",
    "        line = f.readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_ref_list = sorted(id_ref_num.items(), key=lambda item: -item[1])\n",
    "top_id = [item[0] for item in id_ref_list]\n",
    "# map_oddID_newID = dict(zip(top_id, list(range(len(top_id)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# result_dic = {}\n",
    "# dim = 100\n",
    "# word_dic = load_word2vec(dim)\n",
    "import time\n",
    "file = open('/Users/zhengwei/Desktop/small_data.json', 'w') \n",
    "with open(path, 'r') as f:\n",
    "    line = f.readline()\n",
    "    i = 0\n",
    "#    start =time.time()\n",
    "    while line:    \n",
    "        line = json.loads(line)\n",
    "        if \"abstract\" in line and \"references\" in line:\n",
    "            if \"id\" in line and line[\"id\"] in top_id:\n",
    "                i += 1\n",
    "#                 if \"references\" in line and len(line['references']) > 0:\n",
    "#                     new_refe = []\n",
    "#                     for item in line['references']:\n",
    "#                         if item in map_oddID_newID:\n",
    "#                             new_refe.append(map_oddID_newID[item])\n",
    "#                     line['references'] = new_refe\n",
    "                file.write(json.dumps(line))\n",
    "                file.write('\\n')\n",
    "            if i == 100000:\n",
    "                break\n",
    "        line = f.readline()\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1985921"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(top_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\"\"\"--------------Generate the new file with simplified ID----------------------\"\"\"\n",
    "# result_dic = {}\n",
    "# dim = 100\n",
    "# word_dic = load_word2vec(dim)\n",
    "import time\n",
    "# file = open('/Users/zhengwei/Desktop/new_id.json', 'w') \n",
    "all_id = []\n",
    "with open('/Users/zhengwei/Desktop/small_data.json', 'r') as f:\n",
    "    line = f.readline()\n",
    "    i = 0\n",
    "#    start =time.time()\n",
    "    while line:    \n",
    "        line = json.loads(line)\n",
    "        all_id.append(line[\"id\"])\n",
    "        line = f.readline()\n",
    "#         if \"abstract\" in line and \"references\" in line:\n",
    "#             if \"id\" in line and line[\"id\"] in top_id:\n",
    "#                 i += 1\n",
    "# #                 if \"references\" in line and len(line['references']) > 0:\n",
    "# #                     new_refe = []\n",
    "# #                     for item in line['references']:\n",
    "# #                         if item in map_oddID_newID:\n",
    "# #                             new_refe.append(map_oddID_newID[item])\n",
    "# #                     line['references'] = new_refe\n",
    "#                 file.write(json.dumps(line))\n",
    "#                 file.write('\\n')\n",
    "#             if i == 100000:\n",
    "#                 break   \n",
    "# file.close()\n",
    "map_oddID_newID = dict(zip(all_id, list(range(len(all_id)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       " '0fc94460-6b73-4077-9c78-be73c5ce8b40': 789,\n",
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       " '111428dd-2ac9-40c6-8703-419181704ee6': 796,\n",
       " '111fc1c6-a9db-4f08-828f-0026b031fa37': 797,\n",
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       " 'f28fcf23-132d-43b7-a58b-c1ea1745d9a8': 923,\n",
       " 'f2fde098-829b-4697-b3f0-34992a1f5b4c': 924,\n",
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       " 'f4097c9f-8e71-465a-89b6-5b62ce1c53b2': 928,\n",
       " 'f4803b64-b03e-48c1-92f5-353f4aa4cf96': 929,\n",
       " 'f4c6f6c1-6aac-4b0b-a9f0-9b72b54250e2': 930,\n",
       " 'f4d771f0-2189-425a-b56e-c0c4f0972f46': 931,\n",
       " 'f5548f3a-26e1-48b5-a18f-71a23b8f3938': 932,\n",
       " 'f57d5f13-a30b-47e3-9642-cbc74d17a32c': 933,\n",
       " 'f5814492-3cbf-4a08-98eb-0c63689c4729': 934,\n",
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       " 'f80cb798-460e-4619-9b93-c1d5a36761ca': 941,\n",
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       " 'f8fe40d0-e302-42d4-bafa-384ec8b318db': 944,\n",
       " 'f906435a-5fa1-4fd4-b706-dec40b41ebd4': 945,\n",
       " 'f93ee0bc-7029-432f-9234-5cb69bfff56a': 946,\n",
       " 'f94eb31c-a7ad-4256-83bb-44199716b791': 947,\n",
       " 'f9933867-dd22-4d7b-a1d6-689e9397341f': 948,\n",
       " 'f9f164ba-f39f-4d52-9364-85ada52fd5ba': 949,\n",
       " 'fac0e677-4aec-4fc3-9c36-9e05bb9b9a24': 950,\n",
       " 'fac9696d-3642-4970-aa3d-fc096a3d157a': 951,\n",
       " 'fad38e84-53de-4f59-a87a-e8813025cd52': 952,\n",
       " 'fb082f77-fad6-452e-9058-5521c8b814db': 953,\n",
       " 'fb3369bb-9fe2-4e5f-8bb4-e6c0e175caa9': 954,\n",
       " 'fb4ff6da-8185-41c1-bc41-09e75a84905a': 955,\n",
       " 'fbaa81b5-c9d1-45fb-8e6c-bf0bfa65a6ed': 956,\n",
       " 'fbc49801-421f-47a0-a472-48836fd2b3a0': 957,\n",
       " 'fc2cd172-52de-41dd-b366-b404560be258': 958,\n",
       " 'fc3c3c49-6be3-4ca3-9219-ca6c6d4a0707': 959,\n",
       " 'fc57c2af-69ae-4b3c-97e8-95bbb4bb6b3a': 960,\n",
       " 'fd0fec63-a020-4fba-b473-ce507720e624': 961,\n",
       " 'fd8943bf-0e97-4131-b201-3617b1f4a7cf': 962,\n",
       " 'fda1b253-8449-4076-9fc0-42e2fbf67da9': 963,\n",
       " 'fdc8635a-e16c-4438-ab79-3c0ea6b9099c': 964,\n",
       " 'fe28b77e-b279-473a-a75f-5aa04be6b4c1': 965,\n",
       " 'fe809832-a151-4f9b-9a44-15ffb1af21fb': 966,\n",
       " 'fe9d9d16-5181-43d2-aa85-b1c89eb29bfd': 967,\n",
       " 'fea456bd-cf9f-44de-8bc9-99e6f7b3b0aa': 968,\n",
       " 'feb6e44b-384d-495d-9d89-19a6b5d66fad': 969,\n",
       " 'fece11cc-e121-4986-992b-125a2bf87d6f': 970,\n",
       " 'fee460a8-82db-4f2f-8ef8-8ca9b2547b1d': 971,\n",
       " 'ffb09d3d-9d04-4dfb-ba9b-56bcf80e1293': 972,\n",
       " 'ffb7e6ed-1fff-465b-86da-18cd3e0b7a2d': 973,\n",
       " '276cd52d-a23c-46e6-8d86-e26662435b85': 974,\n",
       " '27742253-f1da-41ad-aa4e-2052bedb9f78': 975,\n",
       " '278be97b-7f10-445c-89ee-e15da7380986': 976,\n",
       " '28130d4a-eb5d-475d-b89a-6c8077fee310': 977,\n",
       " '28396fc8-5e87-4ee3-9563-51bb049a541b': 978,\n",
       " '283fe3ef-2563-4daf-8f7b-6470b4f2c42a': 979,\n",
       " '28533f29-5c4f-4ed1-9394-21883fdb468c': 980,\n",
       " '2871ad6f-e6dd-4ec6-9732-bff7a7cdbe15': 981,\n",
       " '28ab7dc8-609c-456e-b15a-1a32186f9f46': 982,\n",
       " '296463ab-ae72-406a-881b-06903ab6cf8f': 983,\n",
       " '29d26f9b-18d0-4ac5-ab05-31a4babb4b03': 984,\n",
       " '2a09c523-6c1c-4dfa-a3e5-6451d722b15b': 985,\n",
       " '2a2fd722-339d-49cd-9285-d27c15a75429': 986,\n",
       " '2a465336-d6d0-48f9-ac24-c202a1e8c6f5': 987,\n",
       " '2b0c56cf-9450-4004-b66d-83e95e42d3ce': 988,\n",
       " '2c6a5f9b-f237-4441-b684-530e33237bfb': 989,\n",
       " '2d0aad27-0eee-480f-9ca9-7bf72b1f5f8c': 990,\n",
       " '2d73eeba-03eb-49b7-a4c6-9f0113ca2d31': 991,\n",
       " '2debd989-5e38-401a-ad47-507f3f4c05e6': 992,\n",
       " '2e54b2c0-e062-4c5a-a8ff-302848c4b423': 993,\n",
       " '2e8fc923-1f46-47b0-becb-3e036dd1522e': 994,\n",
       " '2e9a1085-1947-4990-9d89-08d1a7c101ec': 995,\n",
       " '2f599193-4f1b-46e4-a5bf-c1c5e53739b6': 996,\n",
       " '2f8ad6ec-dee0-4d9f-a3d3-81697fe00a43': 997,\n",
       " '2ff38231-af94-41fb-a16e-ff3dc9efb9f0': 998,\n",
       " '3035d2dd-e675-40ad-83b3-10ff816994a9': 999,\n",
       " ...}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_oddID_newID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open('/Users/zhengwei/Desktop/new_id.json', 'w') \n",
    "with open('/Users/zhengwei/Desktop/small_data.json', 'r') as f:\n",
    "    line = f.readline()\n",
    "    i = 0\n",
    "    while line:    \n",
    "        line = json.loads(line)\n",
    "        line[\"id\"] = map_oddID_newID[line[\"id\"]]\n",
    "        new_ref = []\n",
    "        for item in line[\"references\"]:\n",
    "            if item in map_oddID_newID:\n",
    "                new_ref.append(map_oddID_newID[item])\n",
    "        line[\"references\"] = new_ref\n",
    "        file.write(json.dumps(line))\n",
    "        file.write('\\n')\n",
    "        line = f.readline()\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"-----------------TASK1-3-----------------\"\"\"\n",
    "data = load_pickle('/Users/zhengwei/Desktop/cluster_KMeans.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_data = dict()\n",
    "for key, value in data.items():\n",
    "    if value not in class_data:\n",
    "        class_data[value] = [key]\n",
    "    else:\n",
    "        class_data[value].append(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([3, 2, 0, 6, 5, 4, 8, 9, 1, 7])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
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