nodes 2025 demo

examples/nodes_2025_demo.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8dcd267e",
+   "metadata": {},
+   "source": [
+    "# NODES 2025 Demo\n",
+    "\n",
+    "This notebook represents the demo part of the [presentation](https://neo4j.com/nodes-2025/agenda/easy-jupyter-notebook-graph-visualization-in-10-minutes/) at the NODES 2025 conference."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e15fcce5",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb45b182",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install \"neo4j-viz[gds, neo4j]\" python-dotenv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10b32405",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "# Load credentials for the neo4j database\n",
+    "load_dotenv(\"db_creds.env\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfb884ec",
+   "metadata": {},
+   "source": [
+    "## Building our visualization graph\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02304ee4",
+   "metadata": {},
+   "source": [
+    "### From a Create Query string\n",
+    "\n",
+    "The most simple way to test out neo4j-viz is by using `from_gql_create`.\n",
+    "The nodes and relationships are directly parsed from the provided query string. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05eda814",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neo4j_viz.gql_create import from_gql_create\n",
+    "\n",
+    "VG = from_gql_create(\"\"\"\n",
+    "    CREATE\n",
+    "    (alice:Person {name: 'Alice', age: 30}),\n",
+    "    (bob:Person {name: 'Bob', age: 25}),\n",
+    "    (carol:Person {name: 'Carol', age: 27}),\n",
+    "    (alice)-[:FRIENDS_WITH {since: 2015}]->(bob),\n",
+    "    (bob)-[:FRIENDS_WITH {since: 2018}]->(carol)\n",
+    "\"\"\")\n",
+    "\n",
+    "VG.render(initial_zoom=1.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32979c03",
+   "metadata": {},
+   "source": [
+    "## From a Neo4j database\n",
+    "\n",
+    "Now lets assume, you have a Neo4j database available which you want to inspect.\n",
+    "In the following, I assume the Movies dataset is imported. You can use `:play movies` in the Neo4j Browser to import the dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72b14cea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import neo4j\n",
+    "from neo4j_viz.neo4j import from_neo4j\n",
+    "\n",
+    "driver = neo4j.GraphDatabase.driver(\n",
+    "    uri=os.getenv(\"NEO4J_URI\"),\n",
+    "    auth=(os.getenv(\"NEO4J_USERNAME\"), os.getenv(\"NEO4J_PASSWORD\")),\n",
+    ")\n",
+    "\n",
+    "# Limiting to 20 rows for demo purposes\n",
+    "VG = from_neo4j(driver, row_limit=20)\n",
+    "VG.render(initial_zoom=1.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c7091b4",
+   "metadata": {},
+   "source": [
+    "## From GDS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "153decdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from graphdatascience import GraphDataScience\n",
+    "\n",
+    "gds = GraphDataScience(\n",
+    "    endpoint=os.getenv(\"NEO4J_URI\"),\n",
+    "    auth=(os.getenv(\"NEO4J_USERNAME\"), os.getenv(\"NEO4J_PASSWORD\")),\n",
+    ")\n",
+    "gds.set_database(\"neo4j\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c7cdcb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G, _ = gds.graph.cypher.project(\n",
+    "    query=\"\"\"\n",
+    "    MATCH (s:Person)-[:ACTED_IN]->(m:Movie)<-[:ACTED_IN]-(t:Person)\n",
+    "    WITH s, t, count(m) as common_movies\n",
+    "    WHERE s < t\n",
+    "    RETURN gds.graph.project('demo-graph', s, t, {\n",
+    "                                sourceNodeLabels: labels(s),\n",
+    "                                targetNodeLabels: labels(t),\n",
+    "                                relationshipProperties: {weight: common_movies},\n",
+    "                                relationshipType: 'CO_ACTED'\n",
+    "                }, {undirectedRelationshipTypes: ['CO_ACTED']})\n",
+    "\"\"\"\n",
+    ")\n",
+    "\n",
+    "str(G)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5652cd6",
+   "metadata": {},
+   "source": [
+    "How do we make sure the projection matches our expectation? Especially if the Cypher queries get more complex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1b405bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neo4j_viz.gds import from_gds\n",
+    "\n",
+    "# Limit to a couple of nodes to inspect the projection. Sampling via random-walks implemented in GDS\n",
+    "VG = from_gds(gds, G, db_node_properties=[\"name\"], max_node_count=20)\n",
+    "VG.render()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eabdc19f",
+   "metadata": {},
+   "source": [
+    "Lets run some GDS algorithms to get some more insights about our co-actor graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bfd2bc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gds.pageRank.mutate(G, relationshipWeightProperty=\"weight\", mutateProperty=\"pageRank\")\n",
+    "gds.leiden.mutate(G, mutateProperty=\"component\", relationshipWeightProperty=\"weight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "331f7f79",
+   "metadata": {},
+   "source": [
+    "Other supported datasources include `from_snowflake`, and `from_pandas`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c7da83d",
+   "metadata": {},
+   "source": [
+    "## Customizing the Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c3e5d7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neo4j_viz.gds import from_gds\n",
+    "\n",
+    "# make sure to include also the newly computed properties such as pageRank\n",
+    "# by default from_gds includes all properties of G\n",
+    "VG = from_gds(gds, G, db_node_properties=[\"name\"])\n",
+    "VG.render()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5fc8746",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Lets first fix the caption of the nodes to show the name.\n",
+    "for node in VG.nodes:\n",
+    "    node.caption = node.properties.get(\"name\")\n",
+    "\n",
+    "VG.render()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5bb837b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Inspect the computed communities to see which actors are grouped together\n",
+    "VG.color_nodes(property=\"component\", override=True)\n",
+    "VG.render()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c6839b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Resize nodes based on pageRank property, i.e., more important actors appear larger\n",
+    "VG.resize_nodes(property=\"pageRank\")\n",
+    "VG.render()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8f4e4f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To make sure certain nodes are always visible, we can pin them. Here we pin \"Keanu Reeves\".\n",
+    "pinned_nodes = {\n",
+    "    node.id: True\n",
+    "    for node in VG.nodes\n",
+    "    if node.properties.get(\"name\") in [\"Keanu Reeves\"]\n",
+    "}\n",
+    "\n",
+    "VG.toggle_nodes_pinned(pinned_nodes)\n",
+    "VG.render()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e565d57",
+   "metadata": {},
+   "source": [
+    "Further customization ideas to explore: \n",
+    "\n",
+    "* Modify the layout such as by adding coordinates\n",
+    "* Use custom colors from [`palettable.wesanderson`](https://jiffyclub.github.io/palettable/) \n",
+    "* Change the size range for nodes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "311fbb2f",
+   "metadata": {},
+   "source": [
+    "## Saving the Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efd0a82c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use the save button in the rendered view. This produces a static image.\n",
+    "VG.render()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5128b85",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11201815",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the raw HTML output to a file. This allows to share the interactive visualization.\n",
+    "\n",
+    "import os\n",
+    "from neo4j_viz.options import Renderer\n",
+    "\n",
+    "os.makedirs(\"./out\", exist_ok=True)\n",
+    "\n",
+    "# Save the visualization to a file\n",
+    "with open(\"out/co_acted.html\", \"w\") as f:\n",
+    "    f.write(VG.render(renderer=Renderer.CANVAS).data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71c3f41e",
+   "metadata": {},
+   "source": [
+    "## Cleanup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "322d137f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "driver.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "429dec14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gds.graph.get(\"demo-graph\").drop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "010ab463",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gds.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}