Tag Archives: visualization

The Last Mile

Last-Mile

The “last mile” is a term used in the telecommunications industry that refers to delivering connectivity to the customers that will actually be using the system. In the sense of Graph Databases, it refers to how well the end user can extract value and insight from the graph. We’ve already seen an example of this concept with Graph Search, allowing a user to express their requests in natural language. Today we’ll see another example. We’ll be taking advantage of the features of Neo4j 2.0 to make this work, so be sure to have read the previous post on the matter.

We’re going to be using VisualSearch.js made by Samuel Clay of NewsBlur. VisualSearch.js enhances ordinary search boxes with the ability to autocomplete faceted search queries. It is quite easy to customize and there is an annotated walkthrough of the options available. You can see what it does in the image below, or click it to try their demo.

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Visualizing the news with Vivagraph.js

neo_news

Today I want to introduce you to VivaGraphJS – a JavaScript Graph Drawing Library made by Andrei Kashcha of Yasiv. It supports rendering graphs using WebGL, SVG or CSS formats and currently supports a force directed layout. The Library provides an API which tracks graph changes and reflect changes on the rendering surface which makes it fantastic for graph exploration.

Today we will be integrating it with Neo4j and the Alchemy API.

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CrunchBase on Neo4j

NeoTechnology was featured on TechCrunch after raising a Series B round, and it has an entry on CrunchBase. If you look at CrunchBase closely you’ll notice it’s a graph. Who invested in what, who co-invested, what are the common investment themes between investors, how are companies connected by board members, etc. These are questions we can ask of the graph and are well suited for graph databases.
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Hubway Data Visualization Challenge with Neo4j

Michael Hunger imported the Hubway Challenge dataset into a Neo4j graph database, and made it available for us to play with.
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NeoSocial: Connecting to Facebook with Neo4j

Social applications and Graph Databases go together like peanut butter and jelly. I’m going to walk you through the steps of building an application that connects to Facebook, pulls your friends and likes data and visualizes it. I plan on making a video of me coding it one line at a time, but for now let’s just focus on the main elements.
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Key Players

In the late 90s Michael Jordan, Scottie Pippen, and Dennis Rodman of the Chicago Bulls dominated the basketball court. They were the key players of the Chicago Bulls, and dominated the NBA offensively and defensively. When exploring a social network you’ll want to find who the key players are for a variety of reasons:

Disrupt: Who should be removed from the network to disrupt it?
Protect: Who should be protected in order to keep the network functioning?
Influence: Who should be influenced in order to change social opinion?
Learn: Who should be questioned in order to know what is going on?
Redirect: Who should be moved to alter social flows?
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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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Using Sigma.js with Neo4j

I’ve done a few posts recently using D3.js and now I want to show you how to use two other great Javascript libraries to visualize your graphs. We’ll start with Sigma.js and soon I’ll do another post with Three.js.
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Visualizing a set of Hiveplots with Neo4j


What should a graph look like and how can I tell two graphs apart?

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JUNG in Neo4j – Part 2

A few weeks ago I showed you how to visualize a graph using the chord flare visualization and how to visualize a network using a force directed graph visualization from D3.js.

On Twitter Claire Willett from Riparian Data asked:

This post on Graphs Beyond the Hairball by Robert Kosara explains why some non-traditional graph visualizations may work better and links us to an article explaining what a Node Quilt is and how it’s useful. We’re going to just take the first step and build a Matrix representation of a graph. We will use one of the JUNG clustering algorithms to help us understand it.
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