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Mark Needham
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‎generate_notebook.py

Lines changed: 2 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -125,7 +125,8 @@ def find_tag(file, tag):
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session.write_transaction(lambda tx: tx.run(write_query))''' % write_query_content
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viz_intro_text = '''## Graph Visualisation
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viz_intro_text = '''\
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## Graph Visualisation
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Sometimes a picture can tell more than a table of results and this is often the case with graph algorithms.
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Let's see how to create a graph visualization using neovis.js.

‎notebooks/StronglyConnectedComponents.ipynb

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@@ -13,7 +13,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -31,7 +31,7 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -50,35 +50,28 @@
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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": "stdout",
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"output_type": "stream",
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"text": [
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"Stats: {'labels-added': 6, 'relationships-created': 10, 'nodes-created': 6, 'properties-set': 6}\n"
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]
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}
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],
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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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"create_graph_query = '''\n",
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"CREATE (nAlice:User {id:'Alice'})\n",
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",(nBridget:User {id:'Bridget'})\n",
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",(nCharles:User {id:'Charles'})\n",
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",(nDoug:User {id:'Doug'})\n",
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",(nMark:User {id:'Mark'})\n",
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",(nMichael:User {id:'Michael'})\n",
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"CREATE (nAlice)-[:FOLLOW]->(nBridget)\n",
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",(nAlice)-[:FOLLOW]->(nCharles)\n",
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",(nMark)-[:FOLLOW]->(nDoug)\n",
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",(nMark)-[:FOLLOW]->(nMichael)\n",
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",(nBridget)-[:FOLLOW]->(nMichael)\n",
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",(nDoug)-[:FOLLOW]->(nMark)\n",
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",(nMichael)-[:FOLLOW]->(nAlice)\n",
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",(nAlice)-[:FOLLOW]->(nMichael)\n",
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",(nBridget)-[:FOLLOW]->(nAlice)\n",
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",(nMichael)-[:FOLLOW]->(nBridget);\n",
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"MERGE (nAlice:User {id:'Alice'})\n",
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"MERGE (nBridget:User {id:'Bridget'})\n",
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"MERGE (nCharles:User {id:'Charles'})\n",
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"MERGE (nDoug:User {id:'Doug'})\n",
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"MERGE (nMark:User {id:'Mark'})\n",
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"MERGE (nMichael:User {id:'Michael'})\n",
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"\n",
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"MERGE (nAlice)-[:FOLLOW]->(nBridget)\n",
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"MERGE (nAlice)-[:FOLLOW]->(nCharles)\n",
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"MERGE (nMark)-[:FOLLOW]->(nDoug)\n",
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"MERGE (nMark)-[:FOLLOW]->(nMichael)\n",
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"MERGE (nBridget)-[:FOLLOW]->(nMichael)\n",
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"MERGE (nDoug)-[:FOLLOW]->(nMark)\n",
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"MERGE (nMichael)-[:FOLLOW]->(nAlice)\n",
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"MERGE (nAlice)-[:FOLLOW]->(nMichael)\n",
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"MERGE (nBridget)-[:FOLLOW]->(nAlice)\n",
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"MERGE (nMichael)-[:FOLLOW]->(nBridget);\n",
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"'''\n",
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"\n",
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"with driver.session() as session:\n",
@@ -95,84 +88,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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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>name</th>\n",
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" <th>partition</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>Mark</td>\n",
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" <td>0</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>Michael</td>\n",
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" <td>1</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>Charles</td>\n",
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" <td>2</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>Doug</td>\n",
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" <td>0</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>Alice</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>Bridget</td>\n",
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" <td>1</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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" name partition\n",
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"0 Mark 0\n",
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"1 Michael 1\n",
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"2 Charles 2\n",
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"3 Doug 0\n",
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"4 Alice 1\n",
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"5 Bridget 1"
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]
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},
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"execution_count": 4,
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"streaming_query = \"\"\"\n",
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"CALL algo.scc.stream('User','FOLLOW')\n",
@@ -196,6 +114,157 @@
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"The first and biggest component has members Alice, Bridget, and Michael, while the second component has Doug and Mark.\n",
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"Charles ends up in his own component becuase there isn't an outgoing relationship from that node to any of the others."
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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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"We can also call a version of the algorithm that will store the result as a property on a\n",
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"node. This is useful if we want to run future queries that use the result."
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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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"write_query = \"\"\"\n",
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"CALL algo.scc('User','FOLLOW', {write:true,partitionProperty:'partition'})\n",
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"YIELD loadMillis, computeMillis, writeMillis, setCount, maxSetSize, minSetSize;\n",
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"\"\"\"\n",
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"\n",
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"with driver.session() as session:\n",
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" session.write_transaction(lambda tx: tx.run(write_query))"
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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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"## Graph Visualisation\n",
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"\n",
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"Sometimes a picture can tell more than a table of results and this is often the case with graph algorithms. \n",
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"Let's see how to create a graph visualization using neovis.js.\n",
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"\n",
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"First we'll create a div into which we will generate the visualisation."
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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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"%%html\n",
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"<style type=\"text/css\"> \n",
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".output_wrapper, .output {\n",
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" height:auto !important;\n",
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" max-height:600px;\n",
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"}\n",
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".output_scroll {\n",
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" box-shadow:none !important;\n",
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" webkit-box-shadow:none !important;\n",
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"}\n",
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"\n",
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"#viz {\n",
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" width: 300px;\n",
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" height: 350px;\n",
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" font: 22pt arial;\n",
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"}\n",
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"</style> \n",
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"<div id=\"viz\"></div>"
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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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"Next we need to define the query that the visualization will be generated from, along with config \n",
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"that describes which properties will be used for node size, node colour, and relationship width. \n",
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"\n",
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"We'll then define a JavaScript variable that contains all our parameters."
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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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"from IPython.core.display import Javascript\n",
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"import json\n",
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"from scripts.algo import viz_config, render_image\n",
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"\n",
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"config = viz_config(\"Strongly Connected Components\")\n",
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"query = config[\"query\"]\n",
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"labels_json = config[\"labels_json\"]\n",
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"relationships_json = config[\"relationships_json\"]\n",
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"\n",
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"json_graph = {\n",
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" \"query\": query,\n",
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" \"labels\": labels_json,\n",
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" \"relationships\": relationships_json,\n",
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" \"host\": host,\n",
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" \"user\": user,\n",
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" \"password\": password\n",
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"}\n",
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"\n",
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"Javascript(\"\"\"window.jsonGraph={};\"\"\".format(json.dumps(json_graph)))"
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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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"Now we're ready to call neovis.js and generate our graph visualisation. \n",
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"The following code will create an interactive graph into the div defined above.\n",
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"It will also extract an image representation of the graph and display that in the cell below."
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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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"%%javascript\n",
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"var output_area = this;\n",
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"requirejs(['neovis.js'], function(NeoVis){ \n",
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" var config = {\n",
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" container_id: \"viz\",\n",
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" server_url: window.jsonGraph.host,\n",
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" server_user: window.jsonGraph.user,\n",
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" server_password: window.jsonGraph.password,\n",
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" labels: window.jsonGraph.labels,\n",
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" relationships: window.jsonGraph.relationships,\n",
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" initial_cypher: window.jsonGraph.query\n",
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" };\n",
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" \n",
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" let viz = new NeoVis.default(config);\n",
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" viz.render();\n",
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" \n",
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" viz.onVisualizationRendered(function(ctx) {\n",
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" let imageSrc = ctx.canvas.toDataURL();\n",
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" let kernel = IPython.notebook.kernel;\n",
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" let command = \"image_src = '\" + imageSrc + \"'\";\n",
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" kernel.execute(command);\n",
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" \n",
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" var cell_element = output_area.element.parents('.cell');\n",
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" var cell_idx = Jupyter.notebook.get_cell_elements().index(cell_element);\n",
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" var cell = Jupyter.notebook.get_cell(cell_idx+1);\n",
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" cell.set_text(\"render_image(image_src)\")\n",
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" cell.execute();\n",
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" });\n",
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"});"
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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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}
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],
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"metadata": {},

‎notebooks/scripts/algo.py

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@@ -63,6 +63,21 @@ def viz_config(algorithm_name):
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'caption': False
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}
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}
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},
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"Strongly Connected Components": {
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"query": "MATCH (p1:User)-[r:FOLLOW]->(p2:User) RETURN *",
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"labels_json": {
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'User': {
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'caption': 'id',
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'community': 'partition'
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}
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},
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"relationships_json": {
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'FOLLOW': {
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'thickness': 'weight',
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'caption': False
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}
80+
}
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}
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}[algorithm_name]
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