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Started centrality algos notebook
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‎examples/centrality-algorithms.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Centrality Algorithms"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a target=\"_blank\" href=\"https://colab.research.google.com/github/neo4j/graph-data-science-client/blob/main/examples/centrality-algorithms.ipynb\">\n",
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" <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
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"</a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This Jupyter notebook is hosted [here](https://github.com/neo4j/graph-data-science-client/blob/main/examples/centrality-algorithms.ipynb) in the Neo4j Graph Data Science Client Github repository.\n",
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"\n",
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"Centrality algorithms are used to understand the role or influence of particular nodes in a graph. The notebook shows the application of centrality algorithms using the `graphdatascience` library on the Airline travel reachability network dataset that can be downloaded [here](https://snap.stanford.edu/data/reachability.html).\n",
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"\n",
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"< TALK ABOUT TASKS >\n",
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"\n",
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"### Setup\n",
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"\n",
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"We start by importing our dependencies and setting up our GDS client connection to the database."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Install necessary dependencies\n",
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"#%pip install graphdatascience pandas"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"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",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
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"from graphdatascience import GraphDataScience\n",
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"import pandas as pd\n",
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"import os"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# NEO4J_URI = os.environ.get(\"NEO4J_URI\", \"bolt://localhost:7687\")\n",
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"# NEO4J_AUTH = None\n",
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"# if os.environ.get(\"NEO4J_USER\") and os.environ.get(\"NEO4J_PASSWORD\"):\n",
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"# NEO4J_AUTH = (\n",
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"# os.environ.get(\"NEO4J_USER\"),\n",
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"# os.environ.get(\"NEO4J_PASSWORD\"),\n",
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"# )\n",
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"\n",
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"# gds = GraphDataScience(NEO4J_URI, auth=NEO4J_AUTH)\n",
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"\n",
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"# # Replace with the actual connection URI and credentials\n",
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"NEO4J_CONNECTION_URI = \"bolt://XXXXXXXXXXXXX\n",
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"NEO4J_USERNAME = \"neo4j\"\n",
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"NEO4J_PASSWORD = \"XXXXXXXXXXXXX\"\n",
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"\n",
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"# Client instantiation\n",
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"gds = GraphDataScience(\n",
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" NEO4J_CONNECTION_URI,\n",
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" auth=(NEO4J_USERNAME, NEO4J_PASSWORD)\n",
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")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Importing the dataset\n",
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"\n",
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"We import the dataset as a pandas dataframe first. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>node_id</th>\n",
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" <th>name</th>\n",
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" <th>metro_pop</th>\n",
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" <th>latitude</th>\n",
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" <th>longitude</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0</td>\n",
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" <td>Abbotsford, BC</td>\n",
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" <td>133497.0</td>\n",
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" <td>49.051575</td>\n",
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" <td>-122.328849</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>Aberdeen, SD</td>\n",
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" <td>40878.0</td>\n",
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" <td>45.459090</td>\n",
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" <td>-98.487324</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2</td>\n",
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" <td>Abilene, TX</td>\n",
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" <td>166416.0</td>\n",
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" <td>32.449175</td>\n",
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" <td>-99.741424</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>3</td>\n",
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" <td>Akron/Canton, OH</td>\n",
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" <td>701456.0</td>\n",
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" <td>40.797810</td>\n",
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" <td>-81.371567</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>4</td>\n",
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" <td>Alamosa, CO</td>\n",
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" <td>9433.0</td>\n",
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" <td>37.468180</td>\n",
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" <td>-105.873599</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" node_id name metro_pop latitude longitude\n",
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"0 0 Abbotsford, BC 133497.0 49.051575 -122.328849\n",
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"1 1 Aberdeen, SD 40878.0 45.459090 -98.487324\n",
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"2 2 Abilene, TX 166416.0 32.449175 -99.741424\n",
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"3 3 Akron/Canton, OH 701456.0 40.797810 -81.371567\n",
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"4 4 Alamosa, CO 9433.0 37.468180 -105.873599"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = pd.read_csv('https://snap.stanford.edu/data/reachability-meta.csv.gz', compression='gzip')\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"file_extension": ".py",
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