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Graph Algorithms

Examples in Spark & Neo4j

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Why Neo4j?

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Graph Algorithms: Examples in Spark and Neo4j

Sample code and tips for over 20 practical graph algorithms. Find vulnerabilities, detect communities and improve machine learning.

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Graph Databases for Connected Data

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What is Neo4j?

Neo4j is a native graph database, built from the ground up to leverage not only data but also data relationships. Neo4j connects data as it’s stored, enabling queries never before imagined, at speeds never thought possible.

The Native Graph Advantage

Unlike traditional databases, which arrange data in rows, columns and tables, Neo4j has a flexible structure defined by stored relationships between data records.

With Neo4j, each data record, or node, stores direct pointers to all the nodes it’s connected to. Because Neo4j is designed around this simple, yet powerful optimization, it performs queries with complex connections orders of magnitude faster, and with more depth, than other databases.

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Cypher — The Graph Query Language

With Neo4j, connections between data are stored – not computed at query time. Cypher is a powerful, graph-optimized query language that understands, and takes advantage of, these stored connections.

When trying to find patterns or insights within data, Cypher queries are often much simpler and easier to write than massive SQL JOINs. Since Neo4j doesn’t have tables, there are no JOINs to worry about. For comparison with SQL, here's a simple Cypher query matching all products in a category hierarchy:

Cypher

      MATCH (p:Product)-[:CATEGORY]->(l:ProductCategory)-[:PARENT*0..]->(:ProductCategory {name:"Dairy Products"})
RETURN p.name
    

Here's a similar query in SQL, which is longer and more complex. Unlike Cypher, where depth is unlimited, this SQL query selects just three levels of depth.

SQL

      SELECT p.ProductName
FROM Product AS p
JOIN ProductCategory pc ON (p.CategoryID = pc.CategoryID AND pc.CategoryName = "Dairy Products")

JOIN ProductCategory pc1 ON (p.CategoryID = pc1.CategoryID)
JOIN ProductCategory pc2 ON (pc1.ParentID = pc2.CategoryID AND pc2.CategoryName = "Dairy Products")

JOIN ProductCategory pc3 ON (p.CategoryID = pc3.CategoryID)
JOIN ProductCategory pc4 ON (pc3.ParentID = pc4.CategoryID)
JOIN ProductCategory pc5 ON (pc4.ParentID = pc5.CategoryID AND pc5.CategoryName = "Dairy Products")
;
    

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Use Your Favorite Programming Language

Neo4j officially supports drivers for .Net, Java, JavaScript, Go and Python. Our community contributors provide many more, including PHP, Ruby, R, Erlang, Clojure and C/C++.

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        // npm install --save neo4j-driver
// node example.js
var neo4j = require('neo4j-driver');
var driver = neo4j.driver('bolt://<HOST>:<BOLTPORT>', 
                  neo4j.auth.basic('<USERNAME>', '<PASSWORD>'), 
                  {encrypted: 'ENCRYPTION_OFF'});

var query = 
  `MATCH (movie:Movie)<-[:ACTED_IN]-(actor)-[:ACTED_IN]->(rec:Movie)
   WHERE movie.title IN \$favorites
   RETURN rec.title as title, count(*) as freq 
   ORDER BY freq DESC LIMIT 5`;

var params = {"favorites": ["The Matrix"]};

var session = driver.session();

session.run(query, params)
  .then(function(result) {
    result.records.forEach(function(record) {
        console.log(record.get('title'));
    })
	session.close();
    driver.close();
  })
  .catch(function(error) {
    console.log(error);
  });

      

        # pip install neo4j-driver
# python example.py

from neo4j import GraphDatabase, basic_auth

driver = GraphDatabase.driver(
    "bolt://<HOST>:<BOLTPORT>", 
    auth=basic_auth("<USERNAME>", "<PASSWORD>"))
session = driver.session()

cypher_query = '''
MATCH (movie:Movie)<-[:ACTED_IN]-(actor)-[:ACTED_IN]->(rec:Movie)
WHERE movie.title IN $favorites
RETURN rec.title as title, count(*) as freq 
ORDER BY freq DESC LIMIT 5
'''

results = session.run(cypher_query,
  parameters={"favorites":["The Matrix"]})

for record in results:
  print(record['title'])

      

        //Install-Package Neo4j.Driver -Version 4.0.1
//Connecting to a 3.5.x Neo4j instance

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using Neo4j.Driver;

public class SimpleExample
{
	private readonly IDriver _driver;
	public SimpleExample(string uri, string user, string password)
	{
		_driver = GraphDatabase.Driver(uri, AuthTokens.Basic(user, password), builder => builder.WithEncryptionLevel(EncryptionLevel.None));
	}
	
	public async Task<IEnumerable<string>> GetMovieRecommendations(string favoriteMovieTitle)
	{
		var cypherQuery =
			  @$"MATCH (movie:Movie)<-[:ACTED_IN]-(actor)-[:ACTED_IN]->(rec:Movie) 
				WHERE movie.title IN ${nameof(favoriteMovieTitle)}
				RETURN rec.title AS title, count(*) AS freq
				ORDER BY freq DESC LIMIT 5";
		IAsyncSession session = _driver.AsyncSession();
		var runQuery = await session.ReadTransactionAsync(async tx =>
		{
			var result = await tx.RunAsync(cypherQuery, new { favoriteMovieTitle });
			return await result.ToListAsync();
		});
		await session?.CloseAsync();
		return runQuery.Select(q => q.Values["title"].As<string>());
	}
	
	public static void WriteToConsole(IEnumerable<string> titles)
	{
		foreach (var title in titles)
			Console.WriteLine(title);
	}
	
	public static async Task Main()
	{
		var example = new SimpleExample("bolt://<HOST>:<BOLTPORT>", "<USERNAME>", "<PASSWORD>");
		var movies = await example.GetMovieRecommendations("The Matrix");
		WriteToConsole(movies);
	}
}
      

        // Java Driver Dependency: http://search.maven.org/#artifactdetails|org.neo4j.driver|neo4j-java-driver|4.0.1|jar
// Reactive Streams http://search.maven.org/#artifactdetails|org.reactivestreams|reactive-streams|1.0.3|jar
// javac -cp neo4j-java-driver-*.jar Example.java && java -cp neo4j-java-driver-*.jar:reactive-streams-*.jar:. Example

import org.neo4j.driver.*;
import static org.neo4j.driver.Values.parameters;

import java.util.List;
import static java.util.Arrays.asList;
import static java.util.Collections.singletonMap;

public class Example {

    public static void main(String...args) {
        Config noSSL = Config.builder().withoutEncryption().build();
        try (Driver driver = GraphDatabase.driver("bolt://<HOST>:<BOLTPORT>",AuthTokens.basic("<USERNAME>","<PASSWORD>"),noSSL);
		     Session session = driver.session()) {
            String cypherQuery =
                "MATCH (movie:Movie)<-[:ACTED_IN]-(actor)-[:ACTED_IN]->(rec:Movie) "+
                "WHERE movie.title IN $favorites "+
                "RETURN rec.title as title, count(*) as freq "+
                "ORDER BY freq DESC LIMIT 5";
            Result result = session.run(cypherQuery, parameters("favorites",asList("The Matrix")));
            while (result.hasNext()) {
                System.out.println(result.next().get("title").asString());
            }
	    }
    }
}



      

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Neo4j - The Fastest Path To Graph Success

Neo4j for RDBMS Developers

Get an introduction to Neo4j with The Definitive Guide to Graph Databases for the RDBMS Developer.

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Bottom-line Impact

Learn how companies are using graph databases to gain sustainable competitive advantage.

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Graph Expertise

Tap into an ecosystem of solutions and graph-based services to shorten your learning curve.

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Neo4j - The Fastest Path To Graph Success

Native Graph Database

Experience the leading database engineered from top to bottom for connected data.

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Graph Expertise

Tap into an ecosystem of solutions and graph-based services to shorten your learning curve.

Professional Services

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Join the highest concentration of open source graph innovators on the planet.

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What is Neo4j?

New to Graphs?

From data modeling and drivers to scalability and Cypher queries, learn more about graph database development.

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Neo4j for RDBMS Developers

Get an introduction to Neo4j with The Definitive Guide to Graph Databases for the RDBMS Developer.

Get the Guide

Bottom-line Impact

Learn how companies are using graph databases such as Neo4j to gain sustainable competitive advantage.

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Neo4j Blog
Getting Started with Neo4j Fabric

Fabric is a new feature introduced in Neo4j 4.0 and is a way to store and retrieve data in multiple databases. This feature makes it easy to query the data in the same DBMS or multiple DBMS using a single Cypher query. In practical terms, Fabric provides the infrastructure and tooling ...

Neo4j Blog
The Secret Sauce of Neo4j: Modeling and Querying Graphs

Editor’s Note: This presentation was given by Max De Marzi at GraphConnect New York in September 2018. Presentation Summary Welcome to the world of graphs. If this is one of your first visits and you feel like you don't quite understand what graphs are all about, then continue reading this ...

Neo4j Blog
Rethink Your Master Data: The Limits of Relational Databases

Data is both our most valuable asset and our biggest ongoing challenge. As data grows in volume, variety and complexity, across applications, clouds and siloed systems, traditional ways of working with data no longer work. Increasingly, businesses are recognizing a need to harness all of their data, ...

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July 3, 2020

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Fine-Grained Access Control for Better Security and Privacy

Fine-Grained Access Control for Security and Privacy

The world’s most flexible, reliable and developer–friendly graph database as a service.

Graph Algorithms

Examples in Spark & Neo4j

Why Neo4j?

Free O'Reilly Ebook
Graph Algorithms: Examples in Spark and Neo4j

No Download Required
Get Started Quickly with the Neo4j Sandbox

Free O'Reilly Ebook
Graph Databases for Connected Data

What is Neo4j?

The Native Graph Advantage

Cypher — The Graph Query Language

Cypher

SQL

Use Your Favorite Programming Language

Neo4j - The Fastest Path To Graph Success

Neo4j for RDBMS Developers

Bottom-line Impact

Graph Expertise

Neo4j - The Fastest Path To Graph Success

Native Graph Database

Graph Expertise

Innovation Network

What is Neo4j?

New to Graphs?

Neo4j for RDBMS Developers

Bottom-line Impact

Neo4j Blog
Getting Started with Neo4j Fabric

Neo4j Blog
The Secret Sauce of Neo4j: Modeling and Querying Graphs

Neo4j Blog
Rethink Your Master Data: The Limits of Relational Databases

In the News

Covid-19: Wie Knowledge Graphen die Forschung antreiben

Top developer projects fighting on the front lines of COVID-19

Illuminating the Anonymous with Neo4j’s Graph Database

Taking The Stress Out Of Supply Chains With Graph Technology

Neo4j Events

Case Study: Lockheed Martin

Fine-Grained Access Control for Better Security and Privacy

Fine-Grained Access Control for Security and Privacy

The world’s most flexible, reliable and developer–friendly graph database as a service.

Graph Algorithms

Examples in Spark & Neo4j

Why Neo4j?

Free O'Reilly Ebook Graph Algorithms: Examples in Spark and Neo4j

No Download Required Get Started Quickly with the Neo4j Sandbox

Free O'Reilly Ebook Graph Databases for Connected Data

What is Neo4j?

The Native Graph Advantage

Cypher — The Graph Query Language

Cypher

SQL

Use Your Favorite Programming Language

Neo4j for RDBMS Developers

Bottom-line Impact

Graph Expertise

Native Graph Database

Graph Expertise

Innovation Network

New to Graphs?

Neo4j for RDBMS Developers

Bottom-line Impact

Neo4j Blog Getting Started with Neo4j Fabric

Neo4j Blog The Secret Sauce of Neo4j: Modeling and Querying Graphs

Neo4j Blog Rethink Your Master Data: The Limits of Relational Databases

In the News

Covid-19: Wie Knowledge Graphen die Forschung antreiben

Top developer projects fighting on the front lines of COVID-19

Illuminating the Anonymous with Neo4j’s Graph Database

Taking The Stress Out Of Supply Chains With Graph Technology

Neo4j Events

Free O'Reilly Ebook
Graph Algorithms: Examples in Spark and Neo4j

No Download Required
Get Started Quickly with the Neo4j Sandbox

Free O'Reilly Ebook
Graph Databases for Connected Data

Neo4j Blog
Getting Started with Neo4j Fabric

Neo4j Blog
The Secret Sauce of Neo4j: Modeling and Querying Graphs

Neo4j Blog
Rethink Your Master Data: The Limits of Relational Databases