{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%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": [ "
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GraphNumber of NodesNumber of EdgesAverage DegreeAverage Clustering Coefficientlog NAverage Shortest Path Lengthbetweenness centrality
0Brightkite Checkins Graph719136638071018.9979140.7028548.8805862.4110110.00022
1Gowalla Checkins Graph1070230310456.6443660.5055979.2781865.2229030.000301
2Foursquare EU Checkins Graph202827430376732.7064390.5970979.9174892.28430.000089
3Foursquare IT Checkins Graph3730629749337.6670240.6835658.2241642.1854770.000428
4Brightkite Friendship Graph59283467311.6980430.2197498.6874425.0521620.000448
5(Filtered) Gowalla Friendship Graph8396291226.9371130.2175449.0355114.5585320.000357
6Foursquare IT Friendship Graph207362175.998070.1484897.63675219.5307520.000879
7Foursquare EU Friendship Graph16491594197.2062340.1679469.71057023.7138640.000272
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" ], "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, "metadata": {}, "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" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The first thing that we want to do is very simple, create a random reference for each graph" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" } } }, "nbformat": 4, "nbformat_minor": 2 }