Welcome to this week in Neo4j where we round up what’s been happening in the world of graph databases in the last 7 days.
This week we work out the best tennis player in the world using weighted PageRank, we learn how to do backups on Kubernetes, and how to model financial risks. We also have a great story about using Neo4j to store the storylines of an interactive Theatre Production, and there’s the launch of the Graph Gallery Graph App.
Featured Community Member: Dimitry Solovyov
This weeks featured community member is Dimitry Solovyov.
Dimitry Solovyov – This Week’s Featured Community Member
Dimitry has been working with our partner Neueda. Over the last year he was one of the main contributors to the Cypher for Gremlin transpiler.
Dimitry is an active member of the openCypher implementers group and has frequently presented on the progress of the project at the groups’s meetings and other events.
He also co-presented on the topic at GraphConnect 2017 in NYC in Cypher Everywhere: Neo4j, Hadoop/Spark and the Unexpected
On behalf of the Neo4j community, thanks for all your work Dimitry!
Weighted PageRank with Neo4j Graph Algorithms
This week we released version 18.104.22.168 of the Neo4j Graph Algorithms library, which now has support for weighted PageRank. Tomaz Bratanic and I have written blog posts showing how to use it.
Tomaz beat me to it, showing the difference in using non weighted and weighted PageRank to find the most influential IP addresses on an AT&T Network telecommunications dataset from Kaggle.
I then wrote a blog post where I attempted to reproduce Filippo Radicchi’s paper in which he works out who was the best tennis player ever. Spoiler alert: It’s Roger Federer!
How to Model Financial Risk with a Graph Database
Joe shows how to model investment risk at the trading desk level as a graph, and finishes with a demo of such a model using Neo4j Bloom
Alex Tavgen published an article explaining one of the coolest uses of Neo4j that I’ve come across.
Alex and his team produced a theatre production where the story evolves based on audience participation. After each scene the audience votes and the next scene is based on the outcome of the vote. If they vote for a utopia, it will descend into dystopia.
Behind the scenes they use Neo4j to store a graph of all the scenarios built by the scriptwriters. It’s all wired together to a web application that uses Spring Boot, which has support for Neo4j out of the box.
Extensibility for Java Developers, Kubernetes Backups, Next Generation Chatbots
- Jennifer Reif shared the slides from her talk at the DevUp conference titled Extensibility for Java Developers in Neo4j. Jennifer covers a diverse range of topics, including Spring Data Neo4j, APOC procedures, a Kafka to Neo4j integration, and more.
- David Allen has written a blog post showing How to backup Neo4j Running in Kubernetes using a specialized Docker container that has Neo4j installed and stores the resulting archive in a Google storage bucket.
- Adrián Rivero has written an article titled the next generation of chatbots with NLP services and Graphs. Adrián explains how graphs will sit at the middle of chatbot systems, providing the context needed to answer questions effectively.
- Max De Marzi has written Part 3 of his series on Dynamic Decision Trees. In this post Max shows how to extend the approach to handle cases where not all the facts are known up front, but instead are asked at each step of the tree.
Meet the Graph Gallery – Graph Examples on your Desktop
Graph Apps are single-page applications that takes advantage of some services provided by Neo4j Desktop around the the management of Neo4j Databases.
This week Michael Hunger launched a new Graph App – “Graph Gallery”. It allows you to browse and search Graph Examples (also known as Graph Gists) provided by the Neo4j Community across a variety of use cases and industries.
With a single click, you can launch any of those examples as a Browser Guide in the Neo4j Browser of your currently running database.
Graph Analytics + Graph Viz, Personalising Category Pages
- Elise Devaux has written a blog post showing how to use the PageRank and Louvain algorithms in combination with the Linkurious graph visualisation tool to help find insights into the Panama Papers dataset.
- In Make GitHub great again: Network Analysis with Graph Algorithms, Anicet Hounkpe shows how to execute degree centrality, community detection, and more using Gephi.
- Giridhar Samathipudi has a blog post explaining how to use Neo4j and Redis to personalise the category pages shown to users.
On the podcast: Michael Simons
This week Rik interviewed Michael Simons, a Software Engineer on our Spring Data Neo4j and Neo4j OGM team.
Michael gives an overview of Spring Boot and how Neo4j OGM and Spring Data Neo4j play in that ecosystem, and explains his entry into the world of graphs via jQAssistant, the popular software analytics tool.
Michael also shares his views on the future of the software industry and his plans to build a new talk around the intersection of SQL and Cypher analytical queries. I look forward to seeing that talk when it’s ready!
Want to help build Neo4j as a Service?
The Neo4j Cloud team are growing and need SRE and engineering people to help build and power the managed Neo4j-as-a-service offering.
If you’re interested or know somebody who might be, you can learn more at the link below.
What’s happening next week in the world of graph databases?
October 15th 2018
Tweet of the Week
My favourite tweet this week was by Jessica Kerr:
Don’t forget to RT if you liked it too.
That’s all for this week. Have a great weekend!