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Neo4j Events (Jan/Feb 2015)

16 Jan 2015 by Michal Bachman Neo4j Events Beginner

There is no better way to start 2015 than to learn something new. In the wake of two recent major announcements (here and here), Neo4j is as hot as ever, so it might well be the next skill you pick up or improve. Here’s a list of Neo4j events organised by GraphAware around the world in the next few weeks. We’ll be delighted to see you there!

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Using the Neo4j Browser with Embedded Neo4j

21 Nov 2014 by Luanne Misquitta Neo4j Intermediate

There are times when you have an application using Neo4j in embedded mode but also need to play around with the graph using the Neo4j web browser. Since the database can be accessed from at most one process at a time, trying to start up the Neo4j server when your embedded Neo4j application is running won’t work. The WrappingNeoServerBootstrapper, although deprecated, comes to the rescue. Here’s how to set it up.

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GraphConnect 2014 Talk

20 Nov 2014 by Michal Bachman Neo4j Conference Intermediate

Last month, I had the pleasure of speaking at GraphConnect in San Francisco, introducing the GraphAware Framework to a large audience of Neo4j users and graph enthusiasts. For those who missed the conference, the recording and slides have now been made available. Enjoy and get in touch with feedback / questions!

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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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