GraphAware Blog

Our earlier blog posttalked about using the Neo4j web browser along with embedded Neo4j.The WrappingNeoServerBootstrapper which was employed to do this has been deprecated for a while and it raises questionsabout the alternative.

A common question when planning and designing your Neo4j Graph Database is how to handle “flagged” entities. This couldinclude users that are active, blog posts that are published, news articles that have been read, etc.

There are times when you have an application using Neo4j in embedded mode but also need to play around with the graphusing the Neo4j web browser. Since the database can be accessed from at most one process at a time, trying to start upthe 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.

Specialist in Neo4j consultancy, training, and software development, Graph Aware Ltd has been selected as one of NeoTechnology’s first UK solution partners, under its newly launched partnership program.

In this post, we’d like to introduce the first version of the GraphAware Neo4j ChangeFeed - a GraphAware Runtime Modulethat keeps track of changes made to the graph.

Modelling and querying time-based events in a graph is a fairly common discussion topic and a frequently asked questionon Q/A sites. In this blog post, we evaluate some of the common approaches and introduce GraphAware TimeTree, a GraphAware Framework Module that simplifies modelling time and events in Neo4j.

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

With MERGE set to replace CREATE UNIQUEat some time, the behavior of MERGE can sometimes be tricky to understand.

Efficient counting of relationships in Neo4j was the cornerstone of my Master Thesisand the reason the very first GraphAware Frameworkmodule called the Relationship Count Module was born. The improvements in Neo4j 2.1around dense nodes and the addition of getDegree(…) methods on the Node interface made me eager to do some benchmarking around relationship counts again.

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