diff --git a/examples/gds-example.ipynb b/examples/gds-example.ipynb
index c38d191..77bd74f 100644
--- a/examples/gds-example.ipynb
+++ b/examples/gds-example.ipynb
@@ -1,65 +1,107 @@
{
"cells": [
{
- "cell_type": "markdown",
"metadata": {},
- "source": [
- "# Visualizing Neo4j Graph Data Science (GDS) Graphs"
- ]
+ "cell_type": "markdown",
+ "source": "# Visualizing Neo4j Graph Data Science (GDS) Graphs",
+ "id": "12f02eab9ab5ed32"
},
{
- "cell_type": "code",
- "execution_count": null,
"metadata": {},
+ "cell_type": "code",
"outputs": [],
+ "execution_count": null,
"source": [
"%pip install graphdatascience\n",
"%pip install matplotlib"
- ]
+ ],
+ "id": "2659974f9eb8c448"
},
{
- "cell_type": "markdown",
"metadata": {},
+ "cell_type": "code",
+ "outputs": [],
+ "execution_count": null,
"source": [
- "## Setup GDS graph"
- ]
+ "from dotenv import load_dotenv\n",
+ "\n",
+ "load_dotenv()"
+ ],
+ "id": "a1ba28abfe91b628"
},
{
- "cell_type": "code",
- "execution_count": null,
"metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "## Setup GDS graph\n",
+ "\n",
+ "To use GDS, you can either use GDS as a plugin or Aura Graph Analytics.\n",
+ "In the following, you can choose:\n",
+ "\n",
+ " * Provide Aura API credentials and and use Aura Graph Analytics.\n",
+ " * Use Neo4j + GDS Plugin.\n",
+ "\n",
+ "For more information, see the [GDS documentation](https://neo4j.com/docs/graph-data-science/current/installation/)."
+ ],
+ "id": "c6dc0292966144ba"
+ },
+ {
+ "metadata": {},
+ "cell_type": "code",
"outputs": [],
+ "execution_count": null,
"source": [
"import os\n",
"\n",
+ "from graphdatascience.session import GdsSessions, DbmsConnectionInfo, AuraAPICredentials, SessionMemory\n",
"from graphdatascience import GraphDataScience\n",
"\n",
"# Get Neo4j DB URI, credentials and name from environment if applicable\n",
- "NEO4J_URI = os.environ.get(\"NEO4J_URI\", \"bolt://localhost:7687\")\n",
- "NEO4J_AUTH = (\"neo4j\", None)\n",
- "NEO4J_DB = os.environ.get(\"NEO4J_DB\", \"neo4j\")\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",
- "gds = GraphDataScience(NEO4J_URI, auth=NEO4J_AUTH, database=NEO4J_DB)"
- ]
+ "db_connection = DbmsConnectionInfo(\n",
+ " aura_instance_id=os.environ.get(\"AURA_INSTANCEID\"),\n",
+ " username=os.environ[\"NEO4J_USERNAME\"],\n",
+ " password=os.environ[\"NEO4J_PASSWORD\"],\n",
+ " uri=os.environ[\"NEO4J_URI\"],\n",
+ ")\n",
+ "\n",
+ "session_name = \"neo4j-viz-gds-example\"\n",
+ "if os.environ.get(\"AURA_API_CLIENT_ID\"):\n",
+ " # Use Aura Graph Analytics\n",
+ " sessions = GdsSessions(api_credentials=AuraAPICredentials(\n",
+ " client_id=os.environ[\"AURA_API_CLIENT_ID\"],\n",
+ " client_secret=os.environ[\"AURA_API_CLIENT_SECRET\"],\n",
+ " project_id=os.environ.get(\"AURA_API_PROJECT_ID\"),\n",
+ " ))\n",
+ " gds = sessions.get_or_create(session_name=session_name, memory=SessionMemory.m_2GB, db_connection=db_connection)\n",
+ "else:\n",
+ " # Use GDS Plugin\n",
+ " sessions = None\n",
+ " gds = GraphDataScience(\n",
+ " endpoint=db_connection.get_uri(),\n",
+ " auth=(db_connection.username, db_connection.password),\n",
+ " )"
+ ],
+ "id": "35d47e906f813781"
},
{
+ "metadata": {},
"cell_type": "code",
+ "outputs": [],
"execution_count": null,
+ "source": "G = gds.graph.load_cora(graph_name=\"cora\")",
+ "id": "2ef09c75d64ebfff"
+ },
+ {
"metadata": {},
- "outputs": [],
- "source": [
- "G = gds.graph.load_cora(graph_name=\"cora\")"
- ]
+ "cell_type": "markdown",
+ "source": "",
+ "id": "7d617acd466fc01f"
},
{
- "cell_type": "code",
- "execution_count": null,
"metadata": {},
+ "cell_type": "code",
"outputs": [],
+ "execution_count": null,
"source": [
"# Run some algorithms to use later for visualization\n",
"gds.nodeSimilarity.mutate(\n",
@@ -67,3276 +109,99 @@
")\n",
"gds.pageRank.mutate(G, mutateProperty=\"pagerank\")\n",
"gds.louvain.mutate(G, mutateProperty=\"componentId\")"
- ]
+ ],
+ "id": "d37e9519da4cbfd8"
},
{
- "cell_type": "markdown",
"metadata": {},
- "source": [
- "## Visualization"
- ]
+ "cell_type": "markdown",
+ "source": "## Visualization",
+ "id": "ff2d387d6dccb583"
},
{
- "cell_type": "code",
- "execution_count": null,
"metadata": {},
+ "cell_type": "code",
"outputs": [],
+ "execution_count": null,
"source": [
"from neo4j_viz.gds import from_gds\n",
"\n",
- "VG = from_gds(gds, G, max_node_count=500)"
- ]
+ "VG = from_gds(\n",
+ " gds,\n",
+ " G,\n",
+ " max_node_count=100,\n",
+ ")"
+ ],
+ "id": "7299dbcea5d5b5bd"
},
{
- "cell_type": "code",
- "execution_count": null,
"metadata": {},
- "outputs": [],
- "source": []
- },
- {
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
"outputs": [],
- "source": [
- "len(VG.nodes)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "tags": [
- "preserve-output"
- ]
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " neo4j-viz\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "VG.render()"
- ]
+ "execution_count": null,
+ "source": "VG.render()",
+ "id": "acf2d3dd5e3d1267"
},
{
- "cell_type": "markdown",
"metadata": {},
+ "cell_type": "markdown",
"source": [
"### Changing captions\n",
"\n",
"We can also change the node captions, if we want to see something other that the node labels.\n",
"For this dataset it might make sense to caption by scientific subject."
- ]
+ ],
+ "id": "568a08b5fe175ef1"
},
{
- "cell_type": "code",
- "execution_count": null,
"metadata": {},
+ "cell_type": "code",
"outputs": [],
+ "execution_count": null,
"source": [
"for node in VG.nodes:\n",
" node.caption = str(node.properties[\"subject\"])"
- ]
+ ],
+ "id": "279c18f8799f8928"
},
{
+ "metadata": {},
"cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "tags": [
- "preserve-output"
- ]
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " neo4j-viz\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "VG.render()"
- ]
+ "outputs": [],
+ "execution_count": null,
+ "source": "VG.render()",
+ "id": "e8363f2fe612bf62"
},
{
- "cell_type": "markdown",
"metadata": {},
+ "cell_type": "markdown",
"source": [
"## Sizing the nodes\n",
"\n",
"Next, we can size the nodes by their pageRank score to show their importance."
- ]
+ ],
+ "id": "986d86d1be93033d"
},
{
- "cell_type": "code",
- "execution_count": null,
"metadata": {},
+ "cell_type": "code",
"outputs": [],
+ "execution_count": null,
"source": [
"VG.resize_nodes(property=\"pagerank\")\n",
- "VG.color_nodes(property=\"componentId\")\n",
"VG.render()"
- ]
+ ],
+ "id": "c36d91d10b67b533"
},
{
- "cell_type": "markdown",
"metadata": {},
- "source": [
- "### Coloring"
- ]
+ "cell_type": "markdown",
+ "source": "### Coloring",
+ "id": "d2c237bf1fd2cec9"
},
{
- "cell_type": "markdown",
"metadata": {},
+ "cell_type": "markdown",
"source": [
"There are two main ways of coloring the nodes of a graph:\n",
"\n",
@@ -3344,3229 +209,67 @@
"* By continuous color space, in which case nodes will be colored according to a range of colors, according to their field or property value\n",
"\n",
"We will start be coloring the node based on our discrete node property \"subject\" using the default colors."
- ]
+ ],
+ "id": "5c7e54e9b59699c6"
},
{
+ "metadata": {},
"cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "tags": [
- "preserve-output"
- ]
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " neo4j-viz\n",
- " \n",
- " \n",
- " \n",
- "