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Random Graph Models (Part II)

06 Aug 2014 by Vojtěch Havlíček Neo4j GraphAware Intermediate

In the first part of this short series about random graph models, we talked about why they are useful and had a brief look at two of them: Erdos-Renyi graphs and Barabasi-Albert model. In this post, we take a look at the “small world” phenomenon and another network model, namely the Watts-Strogatz model.

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Random Graph Models (Part I)

16 Jul 2014 by Vojtěch Havlíček Neo4j GraphAware Intermediate

When one obtains a graph data from a measurement on a real world network, it is sometimes useful to make comparison with a random graph. Such graph is characterised by certain degree distribution, which you can imagine to be a list of degrees of nodes present in the network. The most interesting distributions have certain functional dependence which allows one to infer what processes are dominant in formation of the network. The processes consequently characterise the relationships between the nodes.

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Happy Birthday GraphAware

03 Jun 2014 by Michal Bachman Neo4j GraphAware

Today, it is exactly one year ago since Graph Aware Limited was incorporated. It started as a one man show, whilst I was finishing my MSc. Thesis at Imperial College London. Since then, we’ve been growing slowly but steadily and will be moving to our new London office fairly soon (announcements to come). We have happy clients in London, New York, Copenhagen, Barcelona, Prague, and Accra.

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