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{
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"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import os\n",
"import wget\n",
"import zipfile\n",
"import numpy as np\n",
"import pandas as pd\n",
"import networkx as nx\n",
"import plotly.graph_objects as go\n",
"from utils import *\n",
"from collections import Counter\n",
"from tqdm import tqdm\n",
"import time\n",
"\n",
"# ignore warnings\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Graph</th>\n",
" <th>Number of Nodes</th>\n",
" <th>Number of Edges</th>\n",
" <th>Average Degree</th>\n",
" <th>Average Clustering Coefficient</th>\n",
" <th>log N</th>\n",
" <th>Average Shortest Path Length</th>\n",
" <th>betweenness centrality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Brightkite Checkins Graph</td>\n",
" <td>7191</td>\n",
" <td>3663807</td>\n",
" <td>1018.997914</td>\n",
" <td>0.702854</td>\n",
" <td>8.880586</td>\n",
" <td>2.411011</td>\n",
" <td>0.00022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Gowalla Checkins Graph</td>\n",
" <td>10702</td>\n",
" <td>303104</td>\n",
" <td>56.644366</td>\n",
" <td>0.505597</td>\n",
" <td>9.278186</td>\n",
" <td>5.222903</td>\n",
" <td>0.000301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Foursquare EU Checkins Graph</td>\n",
" <td>20282</td>\n",
" <td>7430376</td>\n",
" <td>732.706439</td>\n",
" <td>0.597097</td>\n",
" <td>9.917489</td>\n",
" <td>2.2843</td>\n",
" <td>0.000089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Foursquare IT Checkins Graph</td>\n",
" <td>3730</td>\n",
" <td>629749</td>\n",
" <td>337.667024</td>\n",
" <td>0.683565</td>\n",
" <td>8.224164</td>\n",
" <td>2.185477</td>\n",
" <td>0.000428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Brightkite Friendship Graph</td>\n",
" <td>5928</td>\n",
" <td>34673</td>\n",
" <td>11.698043</td>\n",
" <td>0.219749</td>\n",
" <td>8.687442</td>\n",
" <td>5.052162</td>\n",
" <td>0.000448</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>(Filtered) Gowalla Friendship Graph</td>\n",
" <td>8396</td>\n",
" <td>29122</td>\n",
" <td>6.937113</td>\n",
" <td>0.217544</td>\n",
" <td>9.035511</td>\n",
" <td>4.558532</td>\n",
" <td>0.000357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Foursquare IT Friendship Graph</td>\n",
" <td>2073</td>\n",
" <td>6217</td>\n",
" <td>5.99807</td>\n",
" <td>0.148489</td>\n",
" <td>7.636752</td>\n",
" <td>19.530752</td>\n",
" <td>0.000879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Foursquare EU Friendship Graph</td>\n",
" <td>16491</td>\n",
" <td>59419</td>\n",
" <td>7.206234</td>\n",
" <td>0.167946</td>\n",
" <td>9.710570</td>\n",
" <td>23.713864</td>\n",
" <td>0.000272</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Graph Number of Nodes Number of Edges \\\n",
"0 Brightkite Checkins Graph 7191 3663807 \n",
"1 Gowalla Checkins Graph 10702 303104 \n",
"2 Foursquare EU Checkins Graph 20282 7430376 \n",
"3 Foursquare IT Checkins Graph 3730 629749 \n",
"4 Brightkite Friendship Graph 5928 34673 \n",
"5 (Filtered) Gowalla Friendship Graph 8396 29122 \n",
"6 Foursquare IT Friendship Graph 2073 6217 \n",
"7 Foursquare EU Friendship Graph 16491 59419 \n",
"\n",
" Average Degree Average Clustering Coefficient log N \\\n",
"0 1018.997914 0.702854 8.880586 \n",
"1 56.644366 0.505597 9.278186 \n",
"2 732.706439 0.597097 9.917489 \n",
"3 337.667024 0.683565 8.224164 \n",
"4 11.698043 0.219749 8.687442 \n",
"5 6.937113 0.217544 9.035511 \n",
"6 5.99807 0.148489 7.636752 \n",
"7 7.206234 0.167946 9.710570 \n",
"\n",
" Average Shortest Path Length betweenness centrality \n",
"0 2.411011 0.00022 \n",
"1 5.222903 0.000301 \n",
"2 2.2843 0.000089 \n",
"3 2.185477 0.000428 \n",
"4 5.052162 0.000448 \n",
"5 4.558532 0.000357 \n",
"6 19.530752 0.000879 \n",
"7 23.713864 0.000272 "
]
},
"execution_count": 3,
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"output_type": "execute_result"
}
],
"source": [
"# import the graphs from the saved files\n",
"G_brighkite_checkins = nx.read_gpickle(os.path.join('data', 'brightkite', 'brightkite_checkins_graph.gpickle'))\n",
"G_gowalla_checkins = nx.read_gpickle(os.path.join('data', 'gowalla', 'gowalla_checkins_graph.gpickle'))\n",
"G_foursquareEU_checkins = nx.read_gpickle(os.path.join('data', 'foursquare', 'foursquareEU_checkins_graph.gpickle'))\n",
"G_foursquareIT_checkins = nx.read_gpickle(os.path.join('data', 'foursquare', 'foursquareIT_checkins_graph.gpickle'))\n",
"\n",
"G_brighkite_friends = nx.read_gpickle(os.path.join('data', 'brightkite', 'brightkite_friendships_graph.gpickle'))\n",
"G_gowalla_friends = nx.read_gpickle(os.path.join('data', 'gowalla', 'gowalla_friendships_graph.gpickle'))\n",
"G_foursquareEU_friends = nx.read_gpickle(os.path.join('data', 'foursquare', 'foursquareEU_friendships_graph.gpickle'))\n",
"G_foursquareIT_friends = nx.read_gpickle(os.path.join('data', 'foursquare', 'foursquareIT_friendships_graph.gpickle'))\n",
"\n",
"# open the dataframe object\n",
"analysis_results = pd.read_pickle('analysis_results.pkl')\n",
"analysis_results"
]
},
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"attachments": {},
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"source": [
"The first thing that we want to do is very simple, create a random reference for each graph"
]
}
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