Category Archives: Neography

Graph Generator

Update: Code to this project is available on Github.

In the US Air Guitar Championships, competitors use their talents to fret on an “invisible” guitar to rock a live crowd and deliver a performance that transcends the imitation of a real guitar and becomes an art form in and of itself. The key factor that determines the winner is having the elusive quality of “Airness“. When considering using Neo4j in a project, one of the key considerations is having a domain model that yields itself to a graph representation. In other words, does your data have “Graphiness“. However, it didn’t dawn on me until recently that when starting a proof of concept, you probably don’t have that data (or enough of it) or maybe your security guys won’t let you within 100 miles of the company production data with this newfangled nosql thingamajig.
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Using Three.js with Neo4j

Last week we saw Sigma.js, and as promised here is a graph visualization with Three.js and Neo4j. Three.js is a lightweight 3D library, written by Mr. Doob and a small army of contributors.

The things you can do with Three.js are amazing, and my little demo here doesn’t give it justice, but nonetheless I’ll show you how to build it.
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Max Flow with Gremlin and Transactions

The maximum flow problem was formulated by T.E. Harris as follows:

Consider a rail network connecting two cities by way of a number of intermediate cities, where each link of the network has a number assigned to it representing its capacity. Assuming a steady state condition, a nd a maximal flow from one given city to the other.

Back in the mid 1950s the US Military had an interest in finding out how much capacity the Soviet railway network had to move cargo from the Western Soviet Union to Eastern Europe. This lead to the Maximum Flow problem and the Ford–Fulkerson algorithm to solve it.
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Graph Visualization and Neo4j – Part Three

Like I promised in my previous post, I wanted to do a little something on D3.js.

We are going to take one of their example visualizations and visualize a follows graph.

To create our graph, we will take the names of 20 people: create nodes for them, add them to an index, and randomly link them together.
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Who is the Greatest?

I done wrestled with an alligator, I done tussled with a whale, only last week I murdered a rock, injured a stone, hospitalized a brick. I’m so mean I make medicine sick.

He floats like a butterfly and stings like a bee, but is Muhammad Ali truly the greatest? Greater than the Beatles? Greater than Alexander? Greater than Sliced Bread? Let’s find out.

We begin by requiring neography and creating a function to help us create the greats.

require 'rubygems'
require 'neography'

def create_great(name)
  Neography::Node.create("name" => name)

There are a ton of greats out there, but we’ll keep it simple and create just 8 of the greatest greats.
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Chicago Graph DB Meet-Up

We had our first Graph Database Meet-up in Chicago yesterday!

16 Graphistas came out to learn more about the craft and get an introduction to Neo4j. Ryan Briones from Groupon gave us a venue and helped host the event. No worries if you missed it, your next chance to learn more about Neo4j is coming up on Tuesday February 7th @ 6pm, when Prasanna Pendse will share his experiences with Neo4j at ChicagoRuby: Downtown.

Our next Chicago Graph DB meet-up is tentatively scheduled for February 29th @ 6 pm. This will be a hands-on meet-up. I’ll help you get started with either Neo4j on your laptop or in the cloud with Heroku. We’ll create a few graphs, learn some basic traversals and get comfortable with Neo4j. I’ll have a GitHub repository graph for us to play with and see how you are connected to Kevin Bacon (err I mean Linus Torvalds). He is the center of the GitHub universe right? Right? We’ll let’s find out.

The slides of our first meet-up are available below:

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Neo4j on Heroku – Part Three

This week we learned that leaving the create_graph method accessible to the world was a bad idea. So let’s go ahead and delete that route in Sinatra, and instead create a Rake Task for it.

In Rakefile:

require 'neography/tasks'
require './neoflix.rb'

namespace :neo4j do
  task :create do
    neo =['NEO4J_URL'] || "http://localhost:7474")

That’s much better. We can create our graph locally with
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Neo4j on Heroku – Part Two

We are picking up where we left off on Neo4j on Heroku –Part One so make sure you’ve read it or you’ll be a little lost. So far, we have cloned the Neoflix project, set up our Heroku application and added the Neo4j add-on to our application. We are now ready to populate our graph.

UPDATE: Learn a better way to create the graph in part 3 of my Neo4j on Heroku series.

Bring up two browser windows. On one you’ll go to your Neo4j instance running on Heroku,

$ heroku config
NEO4J_URL      =>

and on the other you’ll go to the create_graph route of your app. So if you named your app neoflix, you’d go to neoflix dot herokuapp dot com/create_graph.

This will run the create_graph method and you’ll see nodes and relationships being created on the Neo4j Dashboard. It’s just over a million relationships, so it will take a few minutes. There are faster ways to load data into Neo4j (wait for part three of this series), but this will work in our case.
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Neo4j on Heroku – Part One

On his blog Marko A. Rodriguez showed us how to make A Graph-Based Movie Recommender Engine with Gremlin and Neo4j.

In this two part series, we are going to take his work from the Gremlin shell and put it on the web using the Heroku Neo4j add-on and altering the Neovigator project for our use case. Heroku has a great article on how to get an example Neo4j application up and running on their Dev Center and Michael Hunger shows you how to add JRuby extensions and provides sample code using the Neo4j.rb Gem by Andreas Ronge.

We are going to follow their recipe, but we are going to add a little spice. Instead of creating a small 2 node, 1 relationship graph, I am going to show you how to leverage the power of Gremlin and Groovy to build a much larger graph from a set of files.

Let’s start by cloning the Neoflix Sinatra application, and instead of installing and starting Neo4j locally, we are going to create a Heroku application, and add Neo4j.

git clone
cd neoflix
bundle install
heroku apps:create neoflix --stack cedar
heroku addons:add neo4j
git push heroku master

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Graph Visualization and Neo4j

So far we’ve learned how to get Neo4j up and running with Neography, how to find friends of friends and degrees of separation with the Neo4j REST API and a little bit of the Gremlin and Cypher languages. However, all we’ve seen is text output. We haven’t really “seen” a graph yet, but that’s about to change.

Vouched holds a graph of skill specific recommendations people have made to each other and visualizes it. I extracted the visualization, and hosted it on github as neovigator. You can get your very own visualization up and running or take a look at this instance running on Heroku.

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