Category Archives: Java

Testing APOC inside your Stored Procedures

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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Parallel K-Hop Counts

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?

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Network Routing in Neo4j

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.

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Calculating the best Rail Road paths in Neo4j

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.

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Neo4j Stored Procedures for Devs that don’t know Java (yet)

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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Dynamic Rule Based Decision Trees in Neo4j – Part 4

So far I’ve only showed you how to traverse a decision tree in Neo4j. The assumption being that you would either create the rules yourself from expert knowledge or via an external algorithm. Today we’re going to add an algorithm to build a decision tree (well a decision stream) right into Neo4j. We will simply pass in the training data and let it build the tree for us. If you are reading this part without reading parts one, two, and three, you should because this builds on what we learned along the way.

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Dynamic Rule Based Decision Trees in Neo4j – Part 3

At Graph Connect this year I did a short lightning talk on building Decision Trees using Neo4j. The slides are up and down below, the video is up. After the talk, someone asked, “What if we don’t know all the facts ahead of time?”. They wanted to be able to step through the tree and ask for the facts as needed at each step. So today we’re going to see how to do that.
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Finding your neighbors using Neo4j

In Mr. Rogers’ Neighborhood, the question “Won’t you be my neighbor?” is an invitation for somebody to be close to you. In graphs, it’s an invitation to traverse. The closest neighbors of a node are those reachable by a single relationship hop, but we can also consider nodes two, three or more hops away our neighbors as well. How can we find them in Neo4j? Using the “star”:
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Multiple origin multiple destination 3 relationships queries for knowledge graphs using Neo4j

The multiple-origin-multiple-destination (MOMD) problem is an NP-Hard problem sometimes seen in logistics planning where paths can stretch out really far. A far simpler problem presents itself when we limit the size of the paths. Now you may be wondering, why would we do that? Well… outside logistics we have plenty of graphs where relevance drops as we get further and further away. Think about an Article on Wikipedia. It has links to many other articles that are relevant, and those have links to other articles that are relevant to them but less relevant to our starting Article, and those have links to other articles that may be relevant to them, but have very little to do with our starting Article. I think if we keep going we end up in Philosophy or something like that.
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Building a Dating site with Neo4j – Part Twelve

It’s time to add “visions of love” to our dating site. So far our posts have been just text status updates and while it is possible to fall in love with someone’s words, it’s harder if they look like the troll that lives under the bridge. So what’s the plan here? Well… like most databases out there, it’s not a good idea to store images in Neo4j. What we are going to store instead is a link to where the image resides… but we also don’t want to deal with having images all over our file system and then having to worry about storage space and replicating them, geographically distributing them for faster access, etc. Hosting images is a problem solved by the use of Content Delivery Networks. So let’s leverage one and build our feature.
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