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small-worlds
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small-worlds/testing.ipynb

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Initial commit, started to build the networks
2 years ago
{
"cells": [
{
"cell_type": "code",
tmp, to remove
2 years ago
"execution_count": 1,
Initial commit, started to build the networks
2 years ago
"metadata": {},
"outputs": [],
"source": [
now we can create edge list in python, started the analysis
2 years ago
"%load_ext autoreload\n",
"%autoreload 2\n",
Initial commit, started to build the networks
2 years ago
"\n",
"import os\n",
"import wget\n",
now we can create edge list in python, started the analysis
2 years ago
"import zipfile\n",
"import numpy as np\n",
"import pandas as pd\n",
Initial commit, started to build the networks
2 years ago
"import networkx as nx\n",
now we can create edge list in python, started the analysis
2 years ago
"import plotly.graph_objects as go\n",
various fixes. Download still broken
2 years ago
"# from utils import *\n",
now we can create edge list in python, started the analysis
2 years ago
"from collections import Counter\n",
"from tqdm import tqdm\n",
"import time\n",
various fixes. Download still broken
2 years ago
"import geopandas as gpd\n",
"import gdown # for downloading files from google drive\n",
now the download function works (only on unix systems). Will be updated to work on Windows too
2 years ago
"import shutil\n",
now we can create edge list in python, started the analysis
2 years ago
"# ignore warnings\n",
"import warnings\n",
various fixes. Download still broken
2 years ago
"import sys\n",
now we can create edge list in python, started the analysis
2 years ago
"warnings.filterwarnings(\"ignore\")"
Initial commit, started to build the networks
2 years ago
]
tmp, to remove
2 years ago
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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" <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>6493</td>\n",
" <td>292973</td>\n",
" <td>90.242723</td>\n",
" <td>0.713999</td>\n",
" <td>8.778480</td>\n",
" <td>3.013369</td>\n",
" <td>0.000534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Gowalla Checkins Graph</td>\n",
" <td>3073</td>\n",
" <td>62790</td>\n",
" <td>40.865604</td>\n",
" <td>0.548372</td>\n",
" <td>8.030410</td>\n",
" <td>3.508031</td>\n",
" <td>0.001277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Foursquare Checkins Graph</td>\n",
" <td>2324</td>\n",
" <td>246702</td>\n",
" <td>212.30809</td>\n",
" <td>0.65273</td>\n",
" <td>7.751045</td>\n",
" <td>2.186112</td>\n",
" <td>0.000938</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Brightkite Friendship Graph</td>\n",
" <td>5420</td>\n",
" <td>14690</td>\n",
" <td>5.420664</td>\n",
" <td>0.218571</td>\n",
" <td>8.597851</td>\n",
" <td>5.231807</td>\n",
" <td>0.000664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>(Filtered) Gowalla Friendship Graph</td>\n",
" <td>2294</td>\n",
" <td>5548</td>\n",
" <td>4.836966</td>\n",
" <td>0.234293</td>\n",
" <td>7.738052</td>\n",
" <td>5.396488</td>\n",
" <td>0.001331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Foursquare Friendship Graph</td>\n",
" <td>1397</td>\n",
" <td>5323</td>\n",
" <td>7.620616</td>\n",
" <td>0.183485</td>\n",
" <td>7.242082</td>\n",
" <td>6.45841</td>\n",
" <td>0.001531</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Graph Number of Nodes Number of Edges \\\n",
"0 Brightkite Checkins Graph 6493 292973 \n",
"1 Gowalla Checkins Graph 3073 62790 \n",
"2 Foursquare Checkins Graph 2324 246702 \n",
"3 Brightkite Friendship Graph 5420 14690 \n",
"4 (Filtered) Gowalla Friendship Graph 2294 5548 \n",
"5 Foursquare Friendship Graph 1397 5323 \n",
"\n",
" Average Degree Average Clustering Coefficient log N \\\n",
"0 90.242723 0.713999 8.778480 \n",
"1 40.865604 0.548372 8.030410 \n",
"2 212.30809 0.65273 7.751045 \n",
"3 5.420664 0.218571 8.597851 \n",
"4 4.836966 0.234293 7.738052 \n",
"5 7.620616 0.183485 7.242082 \n",
"\n",
" Average Shortest Path Length betweenness centrality \n",
"0 3.013369 0.000534 \n",
"1 3.508031 0.001277 \n",
"2 2.186112 0.000938 \n",
"3 5.231807 0.000664 \n",
"4 5.396488 0.001331 \n",
"5 6.45841 0.001531 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_results = pd.read_pickle('analysis_results.pkl')\n",
"analysis_results"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
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" <th>Number of Edges</th>\n",
" <th>Average Degree</th>\n",
" <th>Average Clustering Coefficient</th>\n",
" <th>log N</th>\n",
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" <th>omega-coefficient</th>\n",
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" <th>0</th>\n",
" <td>Brightkite Checkins Graph</td>\n",
" <td>6493</td>\n",
" <td>292973</td>\n",
" <td>90.242723</td>\n",
" <td>0.713999</td>\n",
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" <td>0.000534</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>Gowalla Checkins Graph</td>\n",
" <td>3073</td>\n",
" <td>62790</td>\n",
" <td>40.865604</td>\n",
" <td>0.548372</td>\n",
" <td>8.030410</td>\n",
" <td>3.508031</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Foursquare Checkins Graph</td>\n",
" <td>2324</td>\n",
" <td>246702</td>\n",
" <td>212.30809</td>\n",
" <td>0.65273</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>Brightkite Friendship Graph</td>\n",
" <td>5420</td>\n",
" <td>14690</td>\n",
" <td>5.420664</td>\n",
" <td>0.218571</td>\n",
" <td>8.597851</td>\n",
" <td>5.231807</td>\n",
" <td>0.000664</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>(Filtered) Gowalla Friendship Graph</td>\n",
" <td>2294</td>\n",
" <td>5548</td>\n",
" <td>4.836966</td>\n",
" <td>0.234293</td>\n",
" <td>7.738052</td>\n",
" <td>5.396488</td>\n",
" <td>0.001331</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Foursquare Friendship Graph</td>\n",
" <td>1397</td>\n",
" <td>5323</td>\n",
" <td>7.620616</td>\n",
" <td>0.183485</td>\n",
" <td>7.242082</td>\n",
" <td>6.45841</td>\n",
" <td>0.001531</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Graph Number of Nodes Number of Edges \\\n",
"0 Brightkite Checkins Graph 6493 292973 \n",
"1 Gowalla Checkins Graph 3073 62790 \n",
"2 Foursquare Checkins Graph 2324 246702 \n",
"3 Brightkite Friendship Graph 5420 14690 \n",
"4 (Filtered) Gowalla Friendship Graph 2294 5548 \n",
"5 Foursquare Friendship Graph 1397 5323 \n",
"\n",
" Average Degree Average Clustering Coefficient log N \\\n",
"0 90.242723 0.713999 8.778480 \n",
"1 40.865604 0.548372 8.030410 \n",
"2 212.30809 0.65273 7.751045 \n",
"3 5.420664 0.218571 8.597851 \n",
"4 4.836966 0.234293 7.738052 \n",
"5 7.620616 0.183485 7.242082 \n",
"\n",
" Average Shortest Path Length betweenness centrality omega-coefficient \n",
"0 3.013369 0.000534 NaN \n",
"1 3.508031 0.001277 NaN \n",
"2 2.186112 0.000938 NaN \n",
"3 5.231807 0.000664 NaN \n",
"4 5.396488 0.001331 NaN \n",
"5 6.45841 0.001531 NaN "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_results['omega-coefficient'] = np.nan\n",
"analysis_results"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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" <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",
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" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Brightkite Checkins Graph</td>\n",
" <td>6493</td>\n",
" <td>292973</td>\n",
" <td>90.242723</td>\n",
" <td>0.713999</td>\n",
" <td>8.778480</td>\n",
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" <th>1</th>\n",
" <td>Gowalla Checkins Graph</td>\n",
" <td>3073</td>\n",
" <td>62790</td>\n",
" <td>40.865604</td>\n",
" <td>0.548372</td>\n",
" <td>8.030410</td>\n",
" <td>3.508031</td>\n",
" <td>0.001277</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>Foursquare Checkins Graph</td>\n",
" <td>2324</td>\n",
" <td>246702</td>\n",
" <td>212.30809</td>\n",
" <td>0.65273</td>\n",
" <td>7.751045</td>\n",
" <td>2.186112</td>\n",
" <td>0.000938</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Brightkite Friendship Graph</td>\n",
" <td>5420</td>\n",
" <td>14690</td>\n",
" <td>5.420664</td>\n",
" <td>0.218571</td>\n",
" <td>8.597851</td>\n",
" <td>5.231807</td>\n",
" <td>0.000664</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>(Filtered) Gowalla Friendship Graph</td>\n",
" <td>2294</td>\n",
" <td>5548</td>\n",
" <td>4.836966</td>\n",
" <td>0.234293</td>\n",
" <td>7.738052</td>\n",
" <td>5.396488</td>\n",
" <td>0.001331</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Foursquare Friendship Graph</td>\n",
" <td>1397</td>\n",
" <td>5323</td>\n",
" <td>7.620616</td>\n",
" <td>0.183485</td>\n",
" <td>7.242082</td>\n",
" <td>6.45841</td>\n",
" <td>0.001531</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Graph Number of Nodes Number of Edges \\\n",
"0 Brightkite Checkins Graph 6493 292973 \n",
"1 Gowalla Checkins Graph 3073 62790 \n",
"2 Foursquare Checkins Graph 2324 246702 \n",
"3 Brightkite Friendship Graph 5420 14690 \n",
"4 (Filtered) Gowalla Friendship Graph 2294 5548 \n",
"5 Foursquare Friendship Graph 1397 5323 \n",
"\n",
" Average Degree Average Clustering Coefficient log N \\\n",
"0 90.242723 0.713999 8.778480 \n",
"1 40.865604 0.548372 8.030410 \n",
"2 212.30809 0.65273 7.751045 \n",
"3 5.420664 0.218571 8.597851 \n",
"4 4.836966 0.234293 7.738052 \n",
"5 7.620616 0.183485 7.242082 \n",
"\n",
" Average Shortest Path Length betweenness centrality omega-coefficient \n",
"0 3.013369 0.000534 NaN \n",
"1 3.508031 0.001277 NaN \n",
"2 2.186112 0.000938 NaN \n",
"3 5.231807 0.000664 NaN \n",
"4 5.396488 0.001331 NaN \n",
"5 6.45841 0.001531 NaN "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# rename (Filtered) Gowalla Friendship Graph in Gowalla Friendship Graph\n",
"analysis_results.loc[analysis_results['Graph'] == 'Filtered Gowalla Friendship Graph', 'Graph'] = 'Gowalla Friendship Graph'\n",
"analysis_results"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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" <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",
" <th>omega-coefficient</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Brightkite Checkins Graph</td>\n",
" <td>6493</td>\n",
" <td>292973</td>\n",
" <td>90.242723</td>\n",
" <td>0.713999</td>\n",
" <td>8.778480</td>\n",
" <td>3.013369</td>\n",
" <td>0.000534</td>\n",
" <td>-0.180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Gowalla Checkins Graph</td>\n",
" <td>3073</td>\n",
" <td>62790</td>\n",
" <td>40.865604</td>\n",
" <td>0.548372</td>\n",
" <td>8.030410</td>\n",
" <td>3.508031</td>\n",
" <td>0.001277</td>\n",
" <td>-0.240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Foursquare Checkins Graph</td>\n",
" <td>2324</td>\n",
" <td>246702</td>\n",
" <td>212.30809</td>\n",
" <td>0.65273</td>\n",
" <td>7.751045</td>\n",
" <td>2.186112</td>\n",
" <td>0.000938</td>\n",
" <td>-0.056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Brightkite Friendship Graph</td>\n",
" <td>5420</td>\n",
" <td>14690</td>\n",
" <td>5.420664</td>\n",
" <td>0.218571</td>\n",
" <td>8.597851</td>\n",
" <td>5.231807</td>\n",
" <td>0.000664</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>(Filtered) Gowalla Friendship Graph</td>\n",
" <td>2294</td>\n",
" <td>5548</td>\n",
" <td>4.836966</td>\n",
" <td>0.234293</td>\n",
" <td>7.738052</td>\n",
" <td>5.396488</td>\n",
" <td>0.001331</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Foursquare Friendship Graph</td>\n",
" <td>1397</td>\n",
" <td>5323</td>\n",
" <td>7.620616</td>\n",
" <td>0.183485</td>\n",
" <td>7.242082</td>\n",
" <td>6.45841</td>\n",
" <td>0.001531</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Graph Number of Nodes Number of Edges \\\n",
"0 Brightkite Checkins Graph 6493 292973 \n",
"1 Gowalla Checkins Graph 3073 62790 \n",
"2 Foursquare Checkins Graph 2324 246702 \n",
"3 Brightkite Friendship Graph 5420 14690 \n",
"4 (Filtered) Gowalla Friendship Graph 2294 5548 \n",
"5 Foursquare Friendship Graph 1397 5323 \n",
"\n",
" Average Degree Average Clustering Coefficient log N \\\n",
"0 90.242723 0.713999 8.778480 \n",
"1 40.865604 0.548372 8.030410 \n",
"2 212.30809 0.65273 7.751045 \n",
"3 5.420664 0.218571 8.597851 \n",
"4 4.836966 0.234293 7.738052 \n",
"5 7.620616 0.183485 7.242082 \n",
"\n",
" Average Shortest Path Length betweenness centrality omega-coefficient \n",
"0 3.013369 0.000534 -0.180 \n",
"1 3.508031 0.001277 -0.240 \n",
"2 2.186112 0.000938 -0.056 \n",
"3 5.231807 0.000664 NaN \n",
"4 5.396488 0.001331 NaN \n",
"5 6.45841 0.001531 NaN "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Foursquare Checkins Graph : -0.056\n",
"# Gowalla Checkins Graph : -0.24\n",
"# Brightkite Checkins Graph : -0.18\n",
"\n",
"# add omega-coefficient to the respective graphs\n",
"analysis_results.loc[analysis_results['Graph'] == 'Foursquare Checkins Graph', 'omega-coefficient'] = -0.056\n",
"analysis_results.loc[analysis_results['Graph'] == 'Gowalla Checkins Graph', 'omega-coefficient'] = -0.24\n",
"analysis_results.loc[analysis_results['Graph'] == 'Brightkite Checkins Graph', 'omega-coefficient'] = -0.18\n",
"analysis_results"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# rename (Filtered) Gowalla Friendship Graph in Gowalla Friendship Graph\n",
"analysis_results.loc[analysis_results['Graph'] == '(Filtered) Gowalla Friendship Graph', 'Graph'] = 'Gowalla Friendship Graph'"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# FourSquare Friendship Graph : -0.17\n",
"# Gowalla Friendship Graph : -0.25\n",
"# Brightkite Friendship Graph : -0.20\n",
"\n",
"# add omega-coefficient to the respective graphs\n",
"analysis_results.loc[analysis_results['Graph'] == 'Foursquare Friendship Graph', 'omega-coefficient'] = -0.17\n",
"analysis_results.loc[analysis_results['Graph'] == 'Gowalla Friendship Graph', 'omega-coefficient'] = -0.25\n",
"analysis_results.loc[analysis_results['Graph'] == 'Brightkite Friendship Graph', 'omega-coefficient'] = -0.20"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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",
" <th>omega-coefficient</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Brightkite Checkins Graph</td>\n",
" <td>6493</td>\n",
" <td>292973</td>\n",
" <td>90.242723</td>\n",
" <td>0.713999</td>\n",
" <td>8.778480</td>\n",
" <td>3.013369</td>\n",
" <td>0.000534</td>\n",
" <td>-0.180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Gowalla Checkins Graph</td>\n",
" <td>3073</td>\n",
" <td>62790</td>\n",
" <td>40.865604</td>\n",
" <td>0.548372</td>\n",
" <td>8.030410</td>\n",
" <td>3.508031</td>\n",
" <td>0.001277</td>\n",
" <td>-0.240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Foursquare Checkins Graph</td>\n",
" <td>2324</td>\n",
" <td>246702</td>\n",
" <td>212.30809</td>\n",
" <td>0.65273</td>\n",
" <td>7.751045</td>\n",
" <td>2.186112</td>\n",
" <td>0.000938</td>\n",
" <td>-0.056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Brightkite Friendship Graph</td>\n",
" <td>5420</td>\n",
" <td>14690</td>\n",
" <td>5.420664</td>\n",
" <td>0.218571</td>\n",
" <td>8.597851</td>\n",
" <td>5.231807</td>\n",
" <td>0.000664</td>\n",
" <td>-0.200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Gowalla Friendship Graph</td>\n",
" <td>2294</td>\n",
" <td>5548</td>\n",
" <td>4.836966</td>\n",
" <td>0.234293</td>\n",
" <td>7.738052</td>\n",
" <td>5.396488</td>\n",
" <td>0.001331</td>\n",
" <td>-0.250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Foursquare Friendship Graph</td>\n",
" <td>1397</td>\n",
" <td>5323</td>\n",
" <td>7.620616</td>\n",
" <td>0.183485</td>\n",
" <td>7.242082</td>\n",
" <td>6.45841</td>\n",
" <td>0.001531</td>\n",
" <td>-0.170</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Graph Number of Nodes Number of Edges Average Degree \\\n",
"0 Brightkite Checkins Graph 6493 292973 90.242723 \n",
"1 Gowalla Checkins Graph 3073 62790 40.865604 \n",
"2 Foursquare Checkins Graph 2324 246702 212.30809 \n",
"3 Brightkite Friendship Graph 5420 14690 5.420664 \n",
"4 Gowalla Friendship Graph 2294 5548 4.836966 \n",
"5 Foursquare Friendship Graph 1397 5323 7.620616 \n",
"\n",
" Average Clustering Coefficient log N Average Shortest Path Length \\\n",
"0 0.713999 8.778480 3.013369 \n",
"1 0.548372 8.030410 3.508031 \n",
"2 0.65273 7.751045 2.186112 \n",
"3 0.218571 8.597851 5.231807 \n",
"4 0.234293 7.738052 5.396488 \n",
"5 0.183485 7.242082 6.45841 \n",
"\n",
" betweenness centrality omega-coefficient \n",
"0 0.000534 -0.180 \n",
"1 0.001277 -0.240 \n",
"2 0.000938 -0.056 \n",
"3 0.000664 -0.200 \n",
"4 0.001331 -0.250 \n",
"5 0.001531 -0.170 "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_results\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# save the results into a pickle file\n",
"analysis_results.to_pickle('analysis_results.pkl')"
]
Initial commit, started to build the networks
2 years ago
}
],
"metadata": {
"kernelspec": {
small fixes
2 years ago
"display_name": "Python 3.10.8 64-bit",
Initial commit, started to build the networks
2 years ago
"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",
tmp, to remove
2 years ago
"version": "3.10.9 (main, Dec 19 2022, 17:35:49) [GCC 12.2.0]"
Initial commit, started to build the networks
2 years ago
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
small fixes
2 years ago
"hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
Initial commit, started to build the networks
2 years ago
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}