It’s no secret that one of our hottest use cases lately has been Fraud Detection. A while back we did a webinar talking about some of the ways you could use Neo4j to fight fraud. Watch it, if you haven’t yet. Today I want to augment that webinar with some cypher queries. Let’s see how it works:
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The Awesome Procedures on Cypher (APOC) plugin is packed with goodies to make your life working with Neo4j easier. Sometimes you want to use these goodies in your own stored procedures…and you want to be able to test them. So how do we go about doing that exactly?
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How does the saying go? There are lies, damned lies, and benchmarks. I’ve already made my feelings about database vendor benchmarks known, but in case you missed it. They are complete fabrications. Never to be trusted, never ever. Never. But vendors love to do benchmarks, they love spreading fear, uncertainty and doubt instead of spending their time doing productive things like creating useful content that teaches people how to use their product. I wish I could just ignore this nonsense and focus on what really matters, like helping our customers to successful production rollouts, but alas, here we are.
If you are friends with Jessie, and Jessie is friends with Amy, there is a good chance you’ll eventually become friends with Amy too. In terms of a graph, this would be like a graph with three nodes and two relationships eventually building a third relationship to form a clique. This simple concept is one of the basis for recommendation engines. There are fancy terms for it, like “triadic closure” but basically it just means we are making triangles. But what about Amy’s friend Delilah? Is there a good chance now that you are friends with Amy that you’ll become friends with her? What about Jessie and Delilah? Can we extend the pattern to four nodes or five nodes and go beyond our simple triangle? Continue reading
If you’ve been going through the free Graph Algorithms book from Mark Needham and Amy E. Hodler you’ll eventually get to “Chapter 8: Using Graph Algorithms to Enhance Machine Learning”. This is a long chapter which walks us through how to use Graph Features to build and improve machine learning models. If you need a little help with it, take advantage of this public Jupyter notebook on Anaconda. Give it a shot, and let me know if you run into any issues.
No. Not modeling events, I’m talking about modeling events. Things that happen at different times typically in some known sequence. If you are a long time follower of my blog you know I love promoting the date property of an event into the relationship type to make use of Neo4j’s individual Node-RelationshipType partitioning to speed up my queries, but I’m going to show you something different today.
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Sometimes I contrast Neo4j against relational databases by saying Neo4j is more like a dynamic typed language, and relational databases are more like a static typed language. In Neo4j you don’t have Tables or table definitions, any property can be of any valid value (Java primitives, arrays of Java primitives as well as time and spatial types). Two nodes with the same Label can have completely different properties, and any key can be of any type for different nodes. So for example a User labeled node may have the “id” property be “xyz”, while the “id” property for a Location labeled node may be a spatial type… but another User labeled node may also have the “id” property be a number or an array of floats, or whatever. This kind of freedom can drive people crazy, but it can also be leveraged to make very dynamic applications easy.
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People use Neo4j to manage enterprise architectures all the time. If you haven’t seen this presentation from Thomas Lawrence from Amadeus, then you owe it to yourself to watch it. But what about lower level networks? Can we use Neo4j to model routing in a physical network? Of course we can, and today I’ll show you how.
Did you know that Chicago is the most important railroad center in North America? Chicago has more lines of track radiating in more directions than from any other city. The windy city has long been the most important interchange point for freight traffic between the nation’s major railroads and it is the hub of Amtrak, the intercity rail passenger system. You may not realize it, but railroad tracks and graph theory have a history together. Back in the mid 1950s the US Military had an interest in finding out how much capacity the Soviet railway network had to move cargo from the Western Soviet Union to Eastern Europe. This lead to the Maximum Flow problem and the Ford–Fulkerson algorithm to solve it.
When I joined Neo4j, I didn’t know how to write Java. I was a SQL developer who knew some Ruby and that’s about it. Luckily I had Michael Hunger, Stefan Armbruster, David Montag and others to help me out. I realize however that you may not be so lucky. So today I’m going to share with you a set of slides to help you start you on your journey of using the full power of Neo4j.
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