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Returns outliers detected based on the community structure of the graph.
Returns the number of connected components.
Returns the degree, weighted degree and edge weight distributions.
Returns the communities of the constructed graph as detected by synchronous label propagation.
Returns the communities of a multi-layer graph as detected by synchronous label propagation.
Returns the number of 2-edge temporal motifs for every node in the graph.
Returns the number of triangles and clustering coefficient.
We have updated the Router API to make it cleaner. This applies to Raphtory version 0.11 onwards that will be live from 1st March. Previously the coder needed to say SendUpdate and wrap this around the type of update that was being performed. The API now simply allows the user to directly make the update.
In the previous entry, you learnt how to write your own spout and router to ingest the data. In here, we will show you how to write an analyser that will run algorithms that you could test with your data.
Getting started with Raphtory only takes a few steps. We will use an example Raphtory project and install SBT (Scala Build Tool) to get it up and running.
The first step to getting your first temporal graph analysis up and running is to tell Raphtory how to read your datafile and how to build it into a graph.
In some recent work, we used Raphtory for studying the evolution of a fairly new social network Gab used largely by the alt-right. This blog will discuss our findings with a focus on how Raphtory was used; for a deeper delve into some of the network science take a look at our paper “Moving with the times: Investigating the alt right network Gab using temporal interaction graphs”