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audhiaprilliant/social_network_data.ipynb

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social_network_data.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "unsigned-beverage",
"metadata": {},
"source": [
"# Social Network Data Representation"
]
},
{
"cell_type": "markdown",
"id": "hourly-there",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "convenient-bacon",
"metadata": {},
"source": [
"## Import modules"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "simplified-cooperation",
"metadata": {},
"outputs": [],
"source": [
"# Import module for random number\n",
"from random import randint\n",
"\n",
"# Import module for data manipulation\n",
"import pandas as pd\n",
"\n",
"# Import module for linear algebra\n",
"import numpy as np\n",
"\n",
"# Import module for directory\n",
"import os\n",
"import sys\n",
"\n",
"# Import module fo regular expression\n",
"import re\n",
"\n",
"# Import module for network analysis\n",
"import networkx as nx\n",
"\n",
"# Import module for creating iterators for efficient looping\n",
"import itertools\n",
"\n",
"# Import module for storing collections of data\n",
"import collections\n",
"\n",
"# Import module for data viz\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"id": "efficient-weekend",
"metadata": {},
"source": [
"## Load data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "national-piece",
"metadata": {},
"outputs": [],
"source": [
"# Import the data\n",
"df = pd.read_csv('data/WhatsApp_Chat - Final.csv', sep = ';')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "extensive-compact",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dimension data: 523 rows and 10 columns\n"
]
},
{
"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>datetime</th>\n",
" <th>groupName</th>\n",
" <th>validUID</th>\n",
" <th>UID</th>\n",
" <th>noMobile</th>\n",
" <th>userName</th>\n",
" <th>content</th>\n",
" <th>typeContent</th>\n",
" <th>fromMe</th>\n",
" <th>isAdmin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020-09-24 10:27:49</td>\n",
" <td>Group ABCDE</td>\n",
" <td>&lt;Contact Mr. I ([email protected])&gt;</td>\n",
" <td>[email protected]</td>\n",
" <td>741193172177</td>\n",
" <td>Mr. I</td>\n",
" <td>Lorem ipsum dolor sit amet, consectetur adipis...</td>\n",
" <td>chat</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2020-09-24 10:28:32</td>\n",
" <td>Group ABCDE</td>\n",
" <td>&lt;Contact Mr. I ([email protected])&gt;</td>\n",
" <td>[email protected]</td>\n",
" <td>741193172177</td>\n",
" <td>Mr. I</td>\n",
" <td>Lorem ipsum dolor sit amet, consectetur adipis...</td>\n",
" <td>chat</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020-09-24 10:28:47</td>\n",
" <td>Group ABCDE</td>\n",
" <td>&lt;Contact Mrs. C ([email protected])&gt;</td>\n",
" <td>[email protected]</td>\n",
" <td>666461028713</td>\n",
" <td>Mrs. C</td>\n",
" <td>Lorem ipsum dolor sit amet, consectetur adipis...</td>\n",
" <td>chat</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2020-09-24 10:29:04</td>\n",
" <td>Group ABCDE</td>\n",
" <td>&lt;Contact Mrs. C ([email protected])&gt;</td>\n",
" <td>[email protected]</td>\n",
" <td>666461028713</td>\n",
" <td>Mrs. C</td>\n",
" <td>Lorem ipsum dolor sit amet, consectetur adipis...</td>\n",
" <td>chat</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2020-09-24 10:29:41</td>\n",
" <td>Group ABCDE</td>\n",
" <td>&lt;Contact Mr. I ([email protected])&gt;</td>\n",
" <td>[email protected]</td>\n",
" <td>741193172177</td>\n",
" <td>Mr. I</td>\n",
" <td>Lorem ipsum dolor sit amet, consectetur adipis...</td>\n",
" <td>chat</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" datetime groupName validUID \\\n",
"0 2020-09-24 10:27:49 Group ABCDE <Contact Mr. I ([email protected])> \n",
"1 2020-09-24 10:28:32 Group ABCDE <Contact Mr. I ([email protected])> \n",
"2 2020-09-24 10:28:47 Group ABCDE <Contact Mrs. C ([email protected])> \n",
"3 2020-09-24 10:29:04 Group ABCDE <Contact Mrs. C ([email protected])> \n",
"4 2020-09-24 10:29:41 Group ABCDE <Contact Mr. I ([email protected])> \n",
"\n",
" UID noMobile userName \\\n",
"0 [email protected] 741193172177 Mr. I \n",
"1 [email protected] 741193172177 Mr. I \n",
"2 [email protected] 666461028713 Mrs. C \n",
"3 [email protected] 666461028713 Mrs. C \n",
"4 [email protected] 741193172177 Mr. I \n",
"\n",
" content typeContent fromMe \\\n",
"0 Lorem ipsum dolor sit amet, consectetur adipis... chat False \n",
"1 Lorem ipsum dolor sit amet, consectetur adipis... chat False \n",
"2 Lorem ipsum dolor sit amet, consectetur adipis... chat False \n",
"3 Lorem ipsum dolor sit amet, consectetur adipis... chat False \n",
"4 Lorem ipsum dolor sit amet, consectetur adipis... chat False \n",
"\n",
" isAdmin \n",
"0 True \n",
"1 True \n",
"2 True \n",
"3 True \n",
"4 True "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('Dimension data: {} rows and {} columns'.format(len(df), len(df.columns)))\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "virtual-jacob",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 523 entries, 0 to 522\n",
"Data columns (total 10 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 datetime 523 non-null object\n",
" 1 groupName 523 non-null object\n",
" 2 validUID 523 non-null object\n",
" 3 UID 523 non-null object\n",
" 4 noMobile 523 non-null object\n",
" 5 userName 523 non-null object\n",
" 6 content 521 non-null object\n",
" 7 typeContent 523 non-null object\n",
" 8 fromMe 523 non-null bool \n",
" 9 isAdmin 523 non-null bool \n",
"dtypes: bool(2), object(8)\n",
"memory usage: 33.8+ KB\n"
]
}
],
"source": [
"# Check the data type\n",
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "excited-headline",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"datetime 0\n",
"groupName 0\n",
"validUID 0\n",
"UID 0\n",
"noMobile 0\n",
"userName 0\n",
"content 2\n",
"typeContent 0\n",
"fromMe 0\n",
"isAdmin 0\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check the missing value in the data\n",
"df.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "injured-marine",
"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>datetime</th>\n",
" <th>groupName</th>\n",
" <th>validUID</th>\n",
" <th>UID</th>\n",
" <th>noMobile</th>\n",
" <th>userName</th>\n",
" <th>content</th>\n",
" <th>typeContent</th>\n",
" <th>fromMe</th>\n",
" <th>isAdmin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2020-09-25 19:42:07</td>\n",
" <td>Group ABCDE</td>\n",
" <td>&lt;Contact Mrs. C ([email protected])&gt;</td>\n",
" <td>[email protected]</td>\n",
" <td>666461028713</td>\n",
" <td>Mrs. C</td>\n",
" <td>NaN</td>\n",
" <td>revoked</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>276</th>\n",
" <td>2020-12-07 15:10:11</td>\n",
" <td>Group ABCDE</td>\n",
" <td>&lt;Contact Mr. I ([email protected])&gt;</td>\n",
" <td>[email protected]</td>\n",
" <td>741193172177</td>\n",
" <td>Mr. I</td>\n",
" <td>NaN</td>\n",
" <td>revoked</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" datetime groupName validUID \\\n",
"25 2020-09-25 19:42:07 Group ABCDE <Contact Mrs. C ([email protected])> \n",
"276 2020-12-07 15:10:11 Group ABCDE <Contact Mr. I ([email protected])> \n",
"\n",
" UID noMobile userName content typeContent fromMe \\\n",
"25 [email protected] 666461028713 Mrs. C NaN revoked False \n",
"276 [email protected] 741193172177 Mr. I NaN revoked False \n",
"\n",
" isAdmin \n",
"25 True \n",
"276 True "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Filter the missing value on column of content\n",
"df[df['content'].isna()]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "geological-ridge",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"datetime object\n",
"groupName object\n",
"validUID object\n",
"UID object\n",
"noMobile object\n",
"userName object\n",
"content object\n",
"typeContent object\n",
"dtype: object"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check the data type and scale measurement\n",
"df.select_dtypes(include = ['object']).dtypes"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "everyday-catering",
"metadata": {},
"outputs": [],
"source": [
"# Replace You with the phone number\n",
"df['noMobile'].replace('You', '193360307006', inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "initial-gasoline",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['741193172177', '666461028713', '226238332943', '193360307006',\n",
" '440333782705', '764963914688', '334505867219', '535105070555',\n",
" '211212954659', '757288289714', '914552277711', '414509077840',\n",
" '375867913077', '459930495254', '224853537173', '152028503981',\n",
" '652331228143', '440161492330', '290459901483', '824966872668',\n",
" '438921854219', '827399952327'], dtype=object)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Show the unique phone number\n",
"df['noMobile'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cleared-trick",
"metadata": {},
"outputs": [],
"source": [
"# Extract mention from chat\n",
"def extractMention(x):\n",
" if isinstance(x, str):\n",
" return re.findall(r'@(\\d+)', x)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "pacific-warrior",
"metadata": {},
"outputs": [],
"source": [
"# Extract the phone number by mentions\n",
"df['mention'] = df['content'].apply(extractMention)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "living-democrat",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>datetime</th>\n",
" <th>groupName</th>\n",
" <th>validUID</th>\n",
" <th>UID</th>\n",
" <th>noMobile</th>\n",
" <th>userName</th>\n",
" <th>content</th>\n",
" <th>typeContent</th>\n",
" <th>fromMe</th>\n",
" <th>isAdmin</th>\n",
" <th>mention</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020-09-24 10:28:32</td>\n",
" <td>Group ABCDE</td>\n",
" <td>&lt;Contact Mr. I ([email protected])&gt;</td>\n",
" <td>[email protected]</td>\n",
" <td>741193172177</td>\n",
" <td>Mr. I</td>\n",
" <td>Lorem ipsum dolor sit amet, consectetur adipis...</td>\n",
" <td>chat</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>[226238332943]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2020-09-24 10:28:47</td>\n",
" <td>Group ABCDE</td>\n",
" <td>&lt;Contact Mrs. C ([email protected])&gt;</td>\n",
" <td>[email protected]</td>\n",
" <td>666461028713</td>\n",
" <td>Mrs. C</td>\n",
" <td>Lorem ipsum dolor sit amet, consectetur adipis...</td>\n",
" <td>chat</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>[193360307006]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020-09-24 10:29:41</td>\n",
" <td>Group ABCDE</td>\n",
" <td>&lt;Contact Mr. I ([email protected])&gt;</td>\n",
" <td>[email protected]</td>\n",
" <td>741193172177</td>\n",
" <td>Mr. I</td>\n",
" <td>Lorem ipsum dolor sit amet, consectetur adipis...</td>\n",
" <td>chat</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>[193360307006]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2020-09-24 10:33:27</td>\n",
" <td>Group ABCDE</td>\n",
" <td>&lt;Contact Mrs. L ([email protected])&gt;</td>\n",
" <td>[email protected]</td>\n",
" <td>226238332943</td>\n",
" <td>Mrs. L</td>\n",
" <td>Lorem ipsum dolor sit amet, consectetur adipis...</td>\n",
" <td>chat</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>[334505867219]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2020-09-28 13:44:06</td>\n",
" <td>Group ABCDE</td>\n",
" <td>&lt;Contact Mr. I ([email protected])&gt;</td>\n",
" <td>[email protected]</td>\n",
" <td>741193172177</td>\n",
" <td>Mr. I</td>\n",
" <td>Lorem ipsum dolor sit amet, consectetur adipis...</td>\n",
" <td>chat</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>[193360307006, 535105070555]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" datetime groupName validUID \\\n",
"0 2020-09-24 10:28:32 Group ABCDE <Contact Mr. I ([email protected])> \n",
"1 2020-09-24 10:28:47 Group ABCDE <Contact Mrs. C ([email protected])> \n",
"2 2020-09-24 10:29:41 Group ABCDE <Contact Mr. I ([email protected])> \n",
"3 2020-09-24 10:33:27 Group ABCDE <Contact Mrs. L ([email protected])> \n",
"4 2020-09-28 13:44:06 Group ABCDE <Contact Mr. I ([email protected])> \n",
"\n",
" UID noMobile userName \\\n",
"0 [email protected] 741193172177 Mr. I \n",
"1 [email protected] 666461028713 Mrs. C \n",
"2 [email protected] 741193172177 Mr. I \n",
"3 [email protected] 226238332943 Mrs. L \n",
"4 [email protected] 741193172177 Mr. I \n",
"\n",
" content typeContent fromMe \\\n",
"0 Lorem ipsum dolor sit amet, consectetur adipis... chat False \n",
"1 Lorem ipsum dolor sit amet, consectetur adipis... chat False \n",
"2 Lorem ipsum dolor sit amet, consectetur adipis... chat False \n",
"3 Lorem ipsum dolor sit amet, consectetur adipis... chat False \n",
"4 Lorem ipsum dolor sit amet, consectetur adipis... chat False \n",
"\n",
" isAdmin mention \n",
"0 True [226238332943] \n",
"1 True [193360307006] \n",
"2 True [193360307006] \n",
"3 True [334505867219] \n",
"4 True [193360307006, 535105070555] "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Filter the data in which it has the mention wihtin content\n",
"dfMentioned = df[df['mention'].str.len() > 0]\n",
"dfMentioned.reset_index(drop = True, inplace = True)\n",
"dfMentioned.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "bronze-england",
"metadata": {},
"outputs": [],
"source": [
"# Save the source and target phone number based on mentions\n",
"source = []\n",
"target = []\n",
"\n",
"for i in range(len(dfMentioned)):\n",
" listMentioned = dfMentioned.loc[i]['mention']\n",
" for j in range(len(listMentioned)):\n",
" source.append(dfMentioned.loc[i]['noMobile'])\n",
" target.append(dfMentioned.loc[i]['mention'][j])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "veterinary-assistant",
"metadata": {},
"outputs": [],
"source": [
"# Create a dataframe\n",
"dfSA = pd.DataFrame(\n",
" {\n",
" 'source': source,\n",
" 'target': target\n",
" }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "difficult-musical",
"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>source</th>\n",
" <th>target</th>\n",
" <th>count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>193360307006</td>\n",
" <td>226238332943</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>211212954659</td>\n",
" <td>226238332943</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>224853537173</td>\n",
" <td>193360307006</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>226238332943</td>\n",
" <td>334505867219</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>334505867219</td>\n",
" <td>226238332943</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" source target count\n",
"0 193360307006 226238332943 1\n",
"1 211212954659 226238332943 1\n",
"2 224853537173 193360307006 1\n",
"3 226238332943 334505867219 1\n",
"4 334505867219 226238332943 1"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Count the unique possibilities of two columns\n",
"dfCombination = dfSA.groupby(['source','target']).size().reset_index().rename(columns = {0:'count'})\n",
"dfCombination.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "tested-framing",
"metadata": {},
"outputs": [],
"source": [
"# Graph representation to the adjacency list\n",
"graph = collections.defaultdict(dict)\n",
"\n",
"for row in dfCombination.to_numpy():\n",
" graph[row[0]][row[1]] = row[2]\n",
" graph[row[1]][row[0]] = row[2]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "informal-enhancement",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"defaultdict(dict,\n",
" {'193360307006': {'226238332943': 1,\n",
" '224853537173': 1,\n",
" '440333782705': 2,\n",
" '666461028713': 1,\n",
" '741193172177': 6},\n",
" '226238332943': {'193360307006': 1,\n",
" '211212954659': 1,\n",
" '334505867219': 1,\n",
" '414509077840': 2,\n",
" '440333782705': 2,\n",
" '459930495254': 1,\n",
" '535105070555': 1,\n",
" '741193172177': 3,\n",
" '757288289714': 1,\n",
" '764963914688': 1},\n",
" '211212954659': {'226238332943': 1,\n",
" '440333782705': 1,\n",
" '741193172177': 2},\n",
" '224853537173': {'193360307006': 1,\n",
" '440333782705': 1,\n",
" '666461028713': 1,\n",
" '741193172177': 4},\n",
" '334505867219': {'226238332943': 1},\n",
" '414509077840': {'226238332943': 2,\n",
" '757288289714': 1,\n",
" '764963914688': 1,\n",
" '914552277711': 1,\n",
" '440333782705': 1,\n",
" '535105070555': 1,\n",
" '741193172177': 6},\n",
" '757288289714': {'414509077840': 1,\n",
" '741193172177': 1,\n",
" '226238332943': 1,\n",
" '440333782705': 2},\n",
" '764963914688': {'414509077840': 1,\n",
" '440333782705': 1,\n",
" '741193172177': 2,\n",
" '226238332943': 1,\n",
" '652331228143': 1},\n",
" '914552277711': {'414509077840': 1, '741193172177': 1},\n",
" '440333782705': {'193360307006': 2,\n",
" '211212954659': 1,\n",
" '224853537173': 1,\n",
" '226238332943': 2,\n",
" '414509077840': 1,\n",
" '764963914688': 1,\n",
" '741193172177': 4,\n",
" '757288289714': 2},\n",
" '459930495254': {'226238332943': 1},\n",
" '535105070555': {'226238332943': 1,\n",
" '414509077840': 1,\n",
" '741193172177': 2},\n",
" '666461028713': {'152028503981': 1,\n",
" '193360307006': 1,\n",
" '224853537173': 1,\n",
" '375867913077': 1,\n",
" '741193172177': 8},\n",
" '152028503981': {'666461028713': 1},\n",
" '375867913077': {'666461028713': 1},\n",
" '741193172177': {'193360307006': 6,\n",
" '211212954659': 2,\n",
" '224853537173': 4,\n",
" '226238332943': 3,\n",
" '414509077840': 6,\n",
" '440333782705': 4,\n",
" '535105070555': 2,\n",
" '652331228143': 1,\n",
" '666461028713': 8,\n",
" '757288289714': 1,\n",
" '764963914688': 2,\n",
" '827399952327': 1,\n",
" '914552277711': 1},\n",
" '652331228143': {'741193172177': 1, '764963914688': 1},\n",
" '827399952327': {'741193172177': 1}})"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"graph"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "municipal-sperm",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 1. Determine the figure size\n",
"plt.figure(figsize = (6, 6))\n",
"# 2. Create the graph\n",
"g = nx.from_pandas_edgelist(dfCombination, source = 'source', target = 'target')\n",
"# 3. Create a layout for our nodes \n",
"layout = nx.spring_layout(g, iterations = 50)\n",
"nx.draw(g)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "promising-extension",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['193360307006',\n",
" '211212954659',\n",
" '224853537173',\n",
" '226238332943',\n",
" '334505867219',\n",
" '414509077840',\n",
" '440333782705',\n",
" '459930495254',\n",
" '535105070555',\n",
" '666461028713',\n",
" '741193172177',\n",
" '757288289714',\n",
" '764963914688']"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Make a list of the source, we'll use it later\n",
"sources = list(dfCombination['source'].unique())\n",
"sources"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "muslim-moderator",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['226238332943',\n",
" '193360307006',\n",
" '334505867219',\n",
" '757288289714',\n",
" '764963914688',\n",
" '914552277711',\n",
" '211212954659',\n",
" '224853537173',\n",
" '414509077840',\n",
" '152028503981',\n",
" '375867913077',\n",
" '440333782705',\n",
" '535105070555',\n",
" '652331228143',\n",
" '666461028713',\n",
" '827399952327',\n",
" '741193172177']"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Make a list of the target, we'll use it later\n",
"targets = list(dfCombination['target'].unique())\n",
"targets"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "humanitarian-bidding",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# How many connections does You have coming out of it?\n",
"g.degree('193360307006')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "published-eugene",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"13"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Length of source users\n",
"len(sources)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "dress-privacy",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"17"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Length of target users\n",
"len(targets)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "respected-identity",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['226238332943',\n",
" '193360307006',\n",
" '334505867219',\n",
" '757288289714',\n",
" '764963914688',\n",
" '914552277711',\n",
" '211212954659',\n",
" '224853537173',\n",
" '414509077840',\n",
" '152028503981',\n",
" '375867913077',\n",
" '440333782705',\n",
" '535105070555',\n",
" '652331228143',\n",
" '666461028713',\n",
" '827399952327',\n",
" '741193172177']"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"targets"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "herbal-syracuse",
"metadata": {
"scrolled": false
},
"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>source</th>\n",
" <th>target</th>\n",
" <th>count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>193360307006</td>\n",
" <td>226238332943</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>224853537173</td>\n",
" <td>193360307006</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>440333782705</td>\n",
" <td>193360307006</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>666461028713</td>\n",
" <td>193360307006</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>741193172177</td>\n",
" <td>193360307006</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" source target count\n",
"0 193360307006 226238332943 1\n",
"2 224853537173 193360307006 1\n",
"9 440333782705 193360307006 2\n",
"19 666461028713 193360307006 1\n",
"22 741193172177 193360307006 6"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Detailed interactions\n",
"dfCombination[(dfCombination['source'] == '193360307006') | (dfCombination['target'] == '193360307006')]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "scenic-armstrong",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 1. Determine the figure size\n",
"plt.figure(figsize = (12, 12))\n",
"\n",
"# 2. Create the graph\n",
"g = nx.from_pandas_edgelist(dfCombination, source = 'source', target = 'target')\n",
"\n",
"# 3. Create a layout for our nodes \n",
"layout = nx.spring_layout(g, iterations = 50)\n",
"\n",
"# 4. Draw the parts we want\n",
"# - Edges thin and grey\n",
"# - People small and grey\n",
"# - Source sized according to their number of connections\n",
"# - Source blue\n",
"# - Labels for sources ONLY\n",
"# - Target who are highly connected are a highlighted color\n",
"\n",
"# Go through every sources name, ask the graph how many\n",
"# connections it has. Multiply that by 80 to get the circle size\n",
"source_size = [g.degree(source) * 80 for source in sources]\n",
"nx.draw_networkx_nodes(g, \n",
" layout, \n",
" nodelist = sources,\n",
" node_size = source_size, # a list of sizes, based on g.degree\n",
" node_color = 'orange')\n",
"\n",
"# Draw EVERYONE\n",
"nx.draw_networkx_nodes(g, layout, nodelist = targets, node_color = '#cccccc', node_size = 100)\n",
"\n",
"# Draw POPULAR target\n",
"popular_target = [target for target in targets if g.degree(target) > 1]\n",
"nx.draw_networkx_nodes(g, layout, nodelist = popular_target, node_color = 'red', node_size = 100)\n",
"nx.draw_networkx_edges(g, layout, width = 1, edge_color = '#cccccc')\n",
"node_labels = dict(zip(sources, sources))\n",
"nx.draw_networkx_labels(g, layout, labels = node_labels)\n",
"\n",
"# 5. Turn off the axis because we don't want it\n",
"plt.axis('off')\n",
"plt.title('Group ABCDE')\n",
"\n",
"# 6. Tell matplotlib to show it\n",
"plt.show()"
]
}
],
"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": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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