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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5b60d8ba",
+   "metadata": {},
+   "source": [
+    "# Community Detection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e2fb927",
+   "metadata": {},
+   "source": [
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neo4j/graph-data-science-client/blob/main/examples/community-detection.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f083f11b",
+   "metadata": {},
+   "source": [
+    "This Jupyter notebook is hosted [here](https://github.com/neo4j/graph-data-science-client/blob/main/examples/community-detection.ipynb) in the Neo4j Graph Data Science Client Github repository.\n",
+    "\n",
+    "The notebook shows the usage of the `graphdatascience` library for community detection on the Reddit Hyperlink Network dataset that can be downloaded [here](https://snap.stanford.edu/data/soc-RedditHyperlinks.html). We will use the `soc-redditHyperlinks-body.tsv` file.\n",
+    "\n",
+    "The tasks we cover here include performing initial graph preprocessing using Weakly Connected Components and then performing community detection on the largest component using the Louvain algorithm.\n",
+    "\n",
+    "### Setup\n",
+    "\n",
+    "We need to import the following libraries:\n",
+    "- graphdatascience\n",
+    "- neo4j\n",
+    "- pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3953e353",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\kedar\\anaconda3\\envs\\graph_stuff\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from graphdatascience import GraphDataScience\n",
+    "from neo4j import GraphDatabase\n",
+    "from neo4j.exceptions import ServiceUnavailable\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "46b33d2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NEO4J_URI = os.environ.get(\"NEO4J_URI\", \"bolt://localhost:7687\")\n",
+    "NEO4J_AUTH = None\n",
+    "if os.environ.get(\"NEO4J_USER\") and os.environ.get(\"NEO4J_PASSWORD\"):\n",
+    "    NEO4J_AUTH = (\n",
+    "        os.environ.get(\"NEO4J_USER\"),\n",
+    "        os.environ.get(\"NEO4J_PASSWORD\"),\n",
+    "    )\n",
+    "\n",
+    "gds = GraphDataScience(NEO4J_URI, auth=NEO4J_AUTH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48bd8af1",
+   "metadata": {},
+   "source": [
+    "### Importing the dataset\n",
+    "\n",
+    "We import the dataset as a pandas dataframe first. We work with only a subset of the dataset. The sampled data is only till 1st March 2014. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a8e677aa",
+   "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_SUBREDDIT</th>\n",
+       "      <th>TARGET_SUBREDDIT</th>\n",
+       "      <th>POST_ID</th>\n",
+       "      <th>TIMESTAMP</th>\n",
+       "      <th>LINK_SENTIMENT</th>\n",
+       "      <th>PROPERTIES</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>leagueoflegends</td>\n",
+       "      <td>teamredditteams</td>\n",
+       "      <td>1u4nrps</td>\n",
+       "      <td>2013-12-31 16:39:58</td>\n",
+       "      <td>1</td>\n",
+       "      <td>345.0,298.0,0.75652173913,0.0173913043478,0.08...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>theredlion</td>\n",
+       "      <td>soccer</td>\n",
+       "      <td>1u4qkd</td>\n",
+       "      <td>2013-12-31 18:18:37</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>101.0,98.0,0.742574257426,0.019801980198,0.049...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>inlandempire</td>\n",
+       "      <td>bikela</td>\n",
+       "      <td>1u4qlzs</td>\n",
+       "      <td>2014-01-01 14:54:35</td>\n",
+       "      <td>1</td>\n",
+       "      <td>85.0,85.0,0.752941176471,0.0235294117647,0.082...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>nfl</td>\n",
+       "      <td>cfb</td>\n",
+       "      <td>1u4sjvs</td>\n",
+       "      <td>2013-12-31 17:37:55</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1124.0,949.0,0.772241992883,0.0017793594306,0....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>playmygame</td>\n",
+       "      <td>gamedev</td>\n",
+       "      <td>1u4w5ss</td>\n",
+       "      <td>2014-01-01 02:51:13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>715.0,622.0,0.777622377622,0.00699300699301,0....</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  SOURCE_SUBREDDIT TARGET_SUBREDDIT  POST_ID            TIMESTAMP  \\\n",
+       "0  leagueoflegends  teamredditteams  1u4nrps  2013-12-31 16:39:58   \n",
+       "1       theredlion           soccer   1u4qkd  2013-12-31 18:18:37   \n",
+       "2     inlandempire           bikela  1u4qlzs  2014-01-01 14:54:35   \n",
+       "3              nfl              cfb  1u4sjvs  2013-12-31 17:37:55   \n",
+       "4       playmygame          gamedev  1u4w5ss  2014-01-01 02:51:13   \n",
+       "\n",
+       "   LINK_SENTIMENT                                         PROPERTIES  \n",
+       "0               1  345.0,298.0,0.75652173913,0.0173913043478,0.08...  \n",
+       "1              -1  101.0,98.0,0.742574257426,0.019801980198,0.049...  \n",
+       "2               1  85.0,85.0,0.752941176471,0.0235294117647,0.082...  \n",
+       "3               1  1124.0,949.0,0.772241992883,0.0017793594306,0....  \n",
+       "4               1  715.0,622.0,0.777622377622,0.00699300699301,0....  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('https://snap.stanford.edu/data/soc-redditHyperlinks-body.tsv', sep='\\t')\n",
+    "df = df[df['TIMESTAMP'] < \"2014-03-01 02:51:13\"]\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be7b1c5d",
+   "metadata": {},
+   "source": [
+    "The `LINK_SENTIMENT` column tells if there is a positive (+1) or negative (-1) relationship from the source subreddit to destination subreddit. We filter out the negative sentiment relationships as they won't add to any meaningful communities. We also drop duplicate relationships."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2f153da1",
+   "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_SUBREDDIT</th>\n",
+       "      <th>TARGET_SUBREDDIT</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>leagueoflegends</td>\n",
+       "      <td>teamredditteams</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>inlandempire</td>\n",
+       "      <td>bikela</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>nfl</td>\n",
+       "      <td>cfb</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>playmygame</td>\n",
+       "      <td>gamedev</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>dogemarket</td>\n",
+       "      <td>dogecoin</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  SOURCE_SUBREDDIT TARGET_SUBREDDIT\n",
+       "0  leagueoflegends  teamredditteams\n",
+       "2     inlandempire           bikela\n",
+       "3              nfl              cfb\n",
+       "4       playmygame          gamedev\n",
+       "5       dogemarket         dogecoin"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "relationship_df = df[df['LINK_SENTIMENT'] == 1]\n",
+    "columns = ['SOURCE_SUBREDDIT', 'TARGET_SUBREDDIT']\n",
+    "relationship_df = relationship_df[columns]\n",
+    "relationship_df = relationship_df.drop_duplicates()\n",
+    "relationship_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59e6a5e9",
+   "metadata": {},
+   "source": [
+    "Next, we get a list of all the distinct nodes (source or destination) and load them as a dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6266953f",
+   "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>SUBREDDIT</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>leagueoflegends</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>theredlion</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>inlandempire</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>nfl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>playmygame</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         SUBREDDIT\n",
+       "0  leagueoflegends\n",
+       "1       theredlion\n",
+       "2     inlandempire\n",
+       "3              nfl\n",
+       "4       playmygame"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get unique nodes for each column\n",
+    "source_nodes = pd.Series(df['SOURCE_SUBREDDIT']).unique()\n",
+    "target_nodes = pd.Series(df['TARGET_SUBREDDIT']).unique()\n",
+    "\n",
+    "# get unique nodes for both columns\n",
+    "all_nodes = pd.Series(pd.concat([df['SOURCE_SUBREDDIT'], df['TARGET_SUBREDDIT']])).unique()\n",
+    "\n",
+    "# create new dataframe with distinct nodes\n",
+    "nodes_df = pd.DataFrame({'SUBREDDIT': all_nodes})\n",
+    "nodes_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c30a4378",
+   "metadata": {},
+   "source": [
+    "Finally, we load this data (nodes and edges) into a Graph Database and a GDS graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0b18e522",
+   "metadata": {
+    "scrolled": true
+   },
+   "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gds.run_cypher(\n",
+    "    \"UNWIND $nodes_list AS node_props CREATE (n:Subreddit {name: node_props.SUBREDDIT})\",\n",
+    "    params = {'nodes_list': nodes_df.to_dict('records')})\n",
+    "\n",
+    "gds.run_cypher(\n",
+    "    \"UNWIND $edges_list AS rel_props MATCH (source:Subreddit {name: rel_props.SOURCE_SUBREDDIT}), (target:Subreddit {name: rel_props.TARGET_SUBREDDIT}) CREATE (source)-[:HYPERLINKED_TO {relationship_type: rel_props.relationship_type}]->(target)\", \n",
+    "    params = {'edges_list': relationship_df.to_dict('records')})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7a3509e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading: 100%|██████████| 100.0/100 [00:10<00:00,  9.27%/s] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The projection took 11405 ms\n",
+      "Graph 'reddit' node count: 3801\n",
+      "Graph 'reddit' node labels: ['Subreddit']\n"
+     ]
+    }
+   ],
+   "source": [
+    "node_projection = [\"Subreddit\"]\n",
+    "relationship_projection = {\"HYPERLINKED_TO\": {\"orientation\": \"NATURAL\"}}\n",
+    "\n",
+    "G, result = gds.graph.project(\"reddit\", node_projection, relationship_projection) #, nodeProperties = ['node_id', 'node_label']\n",
+    "\n",
+    "print(f\"The projection took {result['projectMillis']} ms\")\n",
+    "\n",
+    "# We can use convenience methods on `G` to check if the projection looks correct\n",
+    "print(f\"Graph '{G.name()}' node count: {G.node_count()}\")\n",
+    "print(f\"Graph '{G.name()}' node labels: {G.node_labels()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "61aa6afe",
+   "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>degreeDistribution</th>\n",
+       "      <th>graphName</th>\n",
+       "      <th>database</th>\n",
+       "      <th>memoryUsage</th>\n",
+       "      <th>sizeInBytes</th>\n",
+       "      <th>nodeCount</th>\n",
+       "      <th>relationshipCount</th>\n",
+       "      <th>configuration</th>\n",
+       "      <th>density</th>\n",
+       "      <th>creationTime</th>\n",
+       "      <th>modificationTime</th>\n",
+       "      <th>schema</th>\n",
+       "      <th>schemaWithOrientation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>{'p99': 15, 'min': 0, 'max': 87, 'mean': 1.631...</td>\n",
+       "      <td>reddit</td>\n",
+       "      <td>neo4j</td>\n",
+       "      <td>876 KiB</td>\n",
+       "      <td>897448</td>\n",
+       "      <td>3801</td>\n",
+       "      <td>6200</td>\n",
+       "      <td>{'relationshipProjection': {'HYPERLINKED_TO': ...</td>\n",
+       "      <td>0.000429</td>\n",
+       "      <td>2023-05-16T14:07:25.933283995+00:00</td>\n",
+       "      <td>2023-05-16T14:07:27.112253586+00:00</td>\n",
+       "      <td>{'graphProperties': {}, 'relationships': {'HYP...</td>\n",
+       "      <td>{'graphProperties': {}, 'relationships': {'HYP...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  degreeDistribution graphName database  \\\n",
+       "0  {'p99': 15, 'min': 0, 'max': 87, 'mean': 1.631...    reddit    neo4j   \n",
+       "\n",
+       "  memoryUsage  sizeInBytes  nodeCount  relationshipCount  \\\n",
+       "0     876 KiB       897448       3801               6200   \n",
+       "\n",
+       "                                       configuration   density  \\\n",
+       "0  {'relationshipProjection': {'HYPERLINKED_TO': ...  0.000429   \n",
+       "\n",
+       "                          creationTime                     modificationTime  \\\n",
+       "0  2023-05-16T14:07:25.933283995+00:00  2023-05-16T14:07:27.112253586+00:00   \n",
+       "\n",
+       "                                              schema  \\\n",
+       "0  {'graphProperties': {}, 'relationships': {'HYP...   \n",
+       "\n",
+       "                               schemaWithOrientation  \n",
+       "0  {'graphProperties': {}, 'relationships': {'HYP...  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gds.graph.list()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c259471",
+   "metadata": {},
+   "source": [
+    "### Weakly Connected Components\n",
+    "\n",
+    "A graph dataset need not always be connected. That is, there may not exist a path from every node to \n",
+    "every other node in the graph dataset (subgraphs in it may not connected to each other at all). Hence, we \n",
+    "need to find the total number of nodes in each subgraph to see if it is big enough for further graph analysis. \n",
+    "Smaller subgraphs or lone nodes will not contribute to the community detection task and should be \n",
+    "eliminated. Weakly Connected Components is often used as one of the early steps of graph preprocessing.\n",
+    "\n",
+    "We use the [Weakly Connected Components](https://neo4j.com/docs/graph-data-science/2.4-preview/algorithms/wcc/) algorithm to find sets of connected nodes and assign each set a component id."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7a114af1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'jobId': 'c7d9036d-b9a5-4d91-8d95-70bccfd67c2d', 'seedProperty': None, 'consecutiveIds': False, 'threshold': 0.0, 'logProgress': True, 'nodeLabels': ['*'], 'sudo': False, 'relationshipTypes': ['*'], 'mutateProperty': 'componentId', 'concurrency': 4}\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = gds.wcc.mutate(G, mutateProperty='componentId')\n",
+    "print(df.configuration)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "04fd557e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Subreddit    [componentId]\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "G.node_properties()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23a0a487",
+   "metadata": {},
+   "source": [
+    "Next, we will see the size of each connected component and depending on that, we can pick the subgraph that needs further analysis.\n",
+    "\n",
+    "We use `run_cypher` here instead of the direct gds client call since we want to see the size of the connected components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2274a19a",
+   "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>componentId</th>\n",
+       "      <th>communitySize</th>\n",
+       "      <th>subreddit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3172</td>\n",
+       "      <td>[leagueoflegends, nfl, playmygame, dogemarket,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>278</td>\n",
+       "      <td>20</td>\n",
+       "      <td>[orangered, orangeredacademy, pasto_range, per...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>23</td>\n",
+       "      <td>8</td>\n",
+       "      <td>[thedoctorstravels, sirron, aislynisdead, game...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>768</td>\n",
+       "      <td>6</td>\n",
+       "      <td>[iracing, simracing, redditracing, team_medioc...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>832</td>\n",
+       "      <td>6</td>\n",
+       "      <td>[perfumeexchange, indiemakeupandmore, asianbea...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>3712</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[aggies]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>3759</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[brunei]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>3769</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[descentintotyranny]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>3771</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[outofthemetaloop]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>3773</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[pokemonshowdown]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>319 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     componentId  communitySize  \\\n",
+       "0              0           3172   \n",
+       "1            278             20   \n",
+       "2             23              8   \n",
+       "3            768              6   \n",
+       "4            832              6   \n",
+       "..           ...            ...   \n",
+       "314         3712              1   \n",
+       "315         3759              1   \n",
+       "316         3769              1   \n",
+       "317         3771              1   \n",
+       "318         3773              1   \n",
+       "\n",
+       "                                             subreddit  \n",
+       "0    [leagueoflegends, nfl, playmygame, dogemarket,...  \n",
+       "1    [orangered, orangeredacademy, pasto_range, per...  \n",
+       "2    [thedoctorstravels, sirron, aislynisdead, game...  \n",
+       "3    [iracing, simracing, redditracing, team_medioc...  \n",
+       "4    [perfumeexchange, indiemakeupandmore, asianbea...  \n",
+       "..                                                 ...  \n",
+       "314                                           [aggies]  \n",
+       "315                                           [brunei]  \n",
+       "316                               [descentintotyranny]  \n",
+       "317                                 [outofthemetaloop]  \n",
+       "318                                  [pokemonshowdown]  \n",
+       "\n",
+       "[319 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"\"\"\n",
+    "    CALL gds.graph.nodeProperties.stream('reddit', 'componentId')\n",
+    "    YIELD nodeId, propertyValue\n",
+    "    WITH nodeId as nodeId, gds.util.asNode(nodeId).name AS node, propertyValue AS componentId\n",
+    "    WITH componentId, collect(node) AS subreddit, size(collect(nodeId)) AS communitySize\n",
+    "    RETURN componentId, communitySize, subreddit\n",
+    "    ORDER BY communitySize DESC\n",
+    "\"\"\"\n",
+    "\n",
+    "# query = \"\"\"\n",
+    "#     CALL gds.graph.nodeProperties.stream('reddit', 'componentId')\n",
+    "#     YIELD name, propertyValue\n",
+    "#     WITH name as name, gds.util.asNode(name).name AS name, propertyValue AS componentId\n",
+    "#     WITH componentId, collect(name) AS subreddits, size(collect(name)) AS communitySize\n",
+    "#     RETURN componentId, communitySize, subreddits\n",
+    "#     ORDER BY communitySize DESC\n",
+    "# \"\"\"\n",
+    "\n",
+    "# query = \"\"\"\n",
+    "#     CALL gds.wcc.stream('reddit')\n",
+    "#     YIELD nodeId, componentId\n",
+    "#     RETURN componentId, collect(gds.util.asNode(nodeId).node_id) AS Subreddits, size(collect(gds.util.asNode(nodeId).node_id)) AS Num_subreddits\n",
+    "#     ORDER BY size(Subreddits) DESC\n",
+    "# \"\"\"\n",
+    "wcc = gds.run_cypher(query)\n",
+    "wcc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a2355cb",
+   "metadata": {},
+   "source": [
+    "We can see that the component with Id 0 has the max number of subreddits = 3172. So we will work only with that subgraph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d1994b04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Largest_CC, _ = gds.beta.graph.project.subgraph(\n",
+    "      'largest_connected_components', \n",
+    "      G,\n",
+    "      'n.componentId=0', \n",
+    "      '*'\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f3e613a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Graph({'graphName': 'largest_connected_components', 'nodeCount': 3172, 'relationshipCount': 5858, 'database': 'neo4j', 'configuration': {'relationshipProperties': {}, 'creationTime': neo4j.time.DateTime(2023, 5, 16, 14, 43, 14, 779680794, tzinfo=<StaticTzInfo 'Etc/UTC'>), 'validateRelationships': False, 'nodeFilter': 'n.componentId=0', 'relationshipFilter': '*', 'nodeProperties': {}, 'concurrency': 4, 'relationshipProjection': {'HYPERLINKED_TO': {'orientation': 'NATURAL', 'indexInverse': False, 'aggregation': 'DEFAULT', 'type': 'HYPERLINKED_TO', 'properties': {}}}, 'jobId': 'f3f02536-8052-4bc9-b143-ca649e16e0d0', 'nodeProjection': {'Subreddit': {'label': 'Subreddit', 'properties': {}}}, 'logProgress': True, 'readConcurrency': 4, 'sudo': False, 'parameters': {}}, 'schema': {'graphProperties': {}, 'relationships': {'HYPERLINKED_TO': {}}, 'nodes': {'Subreddit': {'componentId': 'Integer (DefaultValue(-9223372036854775808), TRANSIENT)'}}}, 'memoryUsage': '901 KiB'})"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Largest_CC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17942d04",
+   "metadata": {},
+   "source": [
+    "### Community Detection using Louvain\n",
+    "\n",
+    "We use the [Louvain](https://neo4j.com/docs/graph-data-science/2.4-preview/algorithms/louvain/) algorithm to detect communities in our subgraph and assign a louvainCommunityId to each community."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "def26464",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Louvain: 100%|██████████| 100.0/100 [00:11<00:00,  8.74%/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "mutateMillis                                                             4\n",
+       "nodePropertiesWritten                                                 3172\n",
+       "modularity                                                        0.587643\n",
+       "modularities             [0.4494090889646058, 0.5377130147763601, 0.555...\n",
+       "ranLevels                                                               10\n",
+       "communityCount                                                         300\n",
+       "communityDistribution    {'p99': 196, 'min': 1, 'max': 382, 'mean': 10....\n",
+       "postProcessingMillis                                                    17\n",
+       "preProcessingMillis                                                      0\n",
+       "computeMillis                                                        12084\n",
+       "configuration            {'maxIterations': 10, 'seedProperty': None, 'c...\n",
+       "Name: 0, dtype: object"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = gds.louvain.mutate(Largest_CC, mutateProperty='louvainCommunityId')\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7563e824",
+   "metadata": {},
+   "source": [
+    "We get a modularity score of 0.5898 for our community detection algorithm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "858c65be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "writeMillis                                   578\n",
+       "graphName            largest_connected_components\n",
+       "nodeProperties               [louvainCommunityId]\n",
+       "propertiesWritten                            3172\n",
+       "Name: 0, dtype: object"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gds.graph.nodeProperties.write(Largest_CC, [\"louvainCommunityId\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afb104e0",
+   "metadata": {},
+   "source": [
+    "We can also check that the property was written by the below command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "22f73aea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Subreddit    [componentId, louvainCommunityId]\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Largest_CC.node_properties()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "19ccfcb8",
+   "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>communityId</th>\n",
+       "      <th>communitySize</th>\n",
+       "      <th>subreddit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2406</td>\n",
+       "      <td>382</td>\n",
+       "      <td>[airsoft, bandnames, connecticut, thehiddenbar...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2516</td>\n",
+       "      <td>309</td>\n",
+       "      <td>[posthardcore, metalcore, corejerk, iama, karm...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2654</td>\n",
+       "      <td>282</td>\n",
+       "      <td>[locationbot, oldschoolcoolnsfw, uncomfortable...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2676</td>\n",
+       "      <td>196</td>\n",
+       "      <td>[playmygame, circlebroke, tribes, conspiratard...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2546</td>\n",
+       "      <td>185</td>\n",
+       "      <td>[leagueoflegends, kpop, turntablists, minecraf...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>3034</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[screenshots]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>3039</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[leangains]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>3040</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[agnostic]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298</th>\n",
+       "      <td>3043</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[mario]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>3045</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[vegproblems]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>300 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     communityId  communitySize  \\\n",
+       "0           2406            382   \n",
+       "1           2516            309   \n",
+       "2           2654            282   \n",
+       "3           2676            196   \n",
+       "4           2546            185   \n",
+       "..           ...            ...   \n",
+       "295         3034              1   \n",
+       "296         3039              1   \n",
+       "297         3040              1   \n",
+       "298         3043              1   \n",
+       "299         3045              1   \n",
+       "\n",
+       "                                             subreddit  \n",
+       "0    [airsoft, bandnames, connecticut, thehiddenbar...  \n",
+       "1    [posthardcore, metalcore, corejerk, iama, karm...  \n",
+       "2    [locationbot, oldschoolcoolnsfw, uncomfortable...  \n",
+       "3    [playmygame, circlebroke, tribes, conspiratard...  \n",
+       "4    [leagueoflegends, kpop, turntablists, minecraf...  \n",
+       "..                                                 ...  \n",
+       "295                                      [screenshots]  \n",
+       "296                                        [leangains]  \n",
+       "297                                         [agnostic]  \n",
+       "298                                            [mario]  \n",
+       "299                                      [vegproblems]  \n",
+       "\n",
+       "[300 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"\"\"\n",
+    "    CALL gds.graph.nodeProperties.stream('largest_connected_components', 'louvainCommunityId')\n",
+    "    YIELD nodeId, propertyValue\n",
+    "    WITH nodeId as nodeId, gds.util.asNode(nodeId).name AS node, propertyValue AS communityId\n",
+    "    WITH communityId, collect(node) AS subreddit, size(collect(nodeId)) AS communitySize\n",
+    "    RETURN communityId, communitySize, subreddit\n",
+    "    ORDER BY communitySize DESC\n",
+    "\"\"\"\n",
+    "\n",
+    "communities = gds.run_cypher(query)\n",
+    "communities"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65dcb952",
+   "metadata": {},
+   "source": [
+    "### References\n",
+    "S. Kumar, W.L. Hamilton, J. Leskovec, D. Jurafsky. Community Interaction and Conflict on the Web. World Wide Web Conference, 2018."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37b59b5b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}