Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

algorithms

Triangle Count and Clustering Coefficient

Published:

Description

The triangle count algorithm counts the number of triangles (triplet of nodes which all pairwise share a link) that each vertex is a member of, from which additionally the global graph triangle count and clustering coefficient can be realised.

Connected Components

Published:

Description

A connected component of an undirected graph is a set of vertices all of which are connected by paths to each other. This algorithm calculates the number of connected components in the graph and the size of each, and returns some statisics of these.

blog

Dev blog 1

Published:

Placeholder blogpost

documentation

Write your own analysis

In the previous entry, you learnt how to write your own spout and router to ingest the data. In here, you are to learn how to write an analyser that will run algorithms that you want to test with your data. Depending on the type of algorithm that you want to implement, a set of these modules are to be defined: setup, analyse, returnResults, processResults and defineMaxSteps. Briefly;

Deploy Raphtory Locally

You’ve learned so far how to build a docker image of Raphtory and write the necessary bits to ingest and analyse your data – Give yourself a pat on the back!

Installation

In this section, you’re going to go through the steps to have Raphtory up and running on your local machine. To make it as easy as possible, Raphtory can be built into a docker image. For that, you only need to install Docker on your machine.

Building a graph from your data

Now that you have a working version of Raphtory on your machine, the first step to getting your first temporal graph analysis up and running is to tell Raphtory how to read in your datafile and how to build it into a graph. This is done by the Spout and Router classes respectively.

examples