Unleash Data Science. Open Source. Free to use. Yours to extend. Download GraphLab Powergraph 2.2 The GraphLab Project The GraphLab project started at Carnegie Mellon University in 2009 to develop a new parallel computation abstraction tailored to machine learning. GraphLab 1.0 presented our first shared memory design which, through the addition of several matrix factorization toolkits, started to grow a community of users. In the last couple of years, we have focused our development effort on the distributed environment. The latest GraphLab open source release is GraphLab PowerGraph version 2.2, where we introduce the new Warp System which through the use of fine-grained user-mode threading, introduces a new API which brings about a major increase in useability, and will allow us to provide new capabilities more easily in the future.
There are two starting points where one may begin using GraphLab. GraphLab has a large selection of machine learning methods already implemented (GraphLab toolkits). The GraphChi Project. Neo4j. Using Neo4j from Ruby. Getting started with Ruby and Neo4j. Graph Visualization and Neo4j – Part Three. Connections in Time. Some relationships change over time. Think about your friends from high school, college, work, the city you used to live in, the ones that liked you ex- better, etc. When exploring a social network it is important that we understand not only the strength of the relationship now, but over time.
We can use communication between people as a measure. I ran into a visualization that explored how multiple parties where connected by communications in multiple projects. We’re going to reuse it to explore how multiple people interact with each other. So let’s make a network of 50 friends and connect them to each other multiple times. Think of it as people writing on your facebook wall. Let’s give our network a little something special. The code to create a relationship is pretty simple, we’ll use the Batch commands again and reference the nodes we create. Let’s put it together to create our graph. We spent some time getting our data into our graph, now let’s get it all back out. Like this: