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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As a foreigner I was a little perplexed the first time I went to IHOP. You are served a stack of pancakes 3-5 high. How do you eat them? Do you pour syrup over the top and cut down through all the layers and eat them that way… or do you unstack them, pour syrup over each one and eat one at a time? If you are American, you eat them stacked. If you see someone eat them one at a time, you know they are shape-shifting lizard people. But doesn’t that mean the bottom layers are dry and don’t get any butter or syrup on them? Well you would think, but Americans are an ingenious people and they found a way to fix that problem. More syrup, more and more, and then a bit more to be sure… and a side of bacon. Now that you know all about IHOP, let’s switch gears to KHOP. Let’s say you wanted to find out how many nodes there were k-hops away from a starting node. What would be the best way to do that?
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.
