By Mark Needham, Developer Relations Engineer | January 20, 2018
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 have categorical PageRank using graph algorithms, more on knowledge graphs, and an interview with Jesús Barrasa about Neo4j’s new telecoms practice.
Featured Community Member: Alberto Perdomo
Alberto Perdomo – This Week’s Featured Community Member
Alberto has been a member of the Neo4j community since 2012 when he started working on GrapheneDB, who just this week announced improved Neo4j hosting plans on Heroku.
Alberto presented at the first GraphConnect London conference in 2013 and GrapheneDB have been regular sponsors of the conference and associated hackathons in the years since then.
Alberto was interviewed on the Graphistania podcast a couple of years ago where he explained the origin story of GrapheneDB and the challenges building a DBaaS offering. He has also written several articles for the Neo4j blog.
On behalf of the Neo4j community, thanks for all your work Alberto!
From GraphConnect: How Graphs Changed The Way Hackers Attack
BloodHound is one of the coolest tools I’ve come across while writing #twin4j so it was really interesting to hear about the problem it aims to solve and how it came to be.
Categorical PageRank, Product catalog graph, Knowledge graphs
- In early 2015 Kenny Bastani introduced the concept of categorical PageRank and showed how to calculate it for Wikipedia pages using Neo4j Mazerunner. This week Tomaz Bratanic wrote a post showing how to apply categorical PageRank to a Game of Thrones dataset using Neo4j graph algorithms.
- Thomas Frisendal has another installment of his series on Knowledge Graphs. In this post Thomas wonders why knowledge graphs are getting so much attention and describes an architecture for the finance industry with a knowledge graph at its heart.
On the podcast: Jesús Barrasa
Jesus explains the common use cases he sees for graphs in the telecom space such as dependency modelling and root cause analysis and his hopes that graphs will become ubiquitous in this space.
Visualizing PE files, Dynamic rule based decision trees, Kotlin
- Michael Hunger shared the slides from his presentation Building Community APIs using GraphQL, Neo4j, and Kotlin at the Cincinnati Kotlin User Group.
- Sam Brown created import_vis, a tool that can be used to visualise and query imports and exports in Windows Portable Executable (PE) files.
- Max De Marzi wrote a blog post in which he showed how to build a dynamic rule based decision trees using Neo4j. In the post Max explains how to write a procedure that explores rules using the Neo4j Traversal API and evaluates predicate expressions using the Janino Java compiler.
- Max also wrote a post in which he shows how you might go about building a high performance in memory graph database.
What’s happening next week in the world of graph databases?
January 23rd 2018
Oleg Shilovitsky, Christopher Chaulk
January 24th 2018
January 25th 2018
Eric Wespi, Nathan Adams, Eric Spiegelberg
Tweet of the Week
My favourite tweet this week was by David Moore:
#neo4j graph database of co-purchased products@noe4j using the neo4j browser to explore a graph is amazing. One click to explore a relationship. Use colors for nodes and relationships.#awesome #NoSQL pic.twitter.com/be8wHoYzal— David Moore (@Mooredvdcoll) January 12, 2018
Don’t forget to RT if you liked it too.
That’s all for this week. Have a great weekend!
About the Author
Mark Needham, Developer Relations Engineer
Mark Needham is a graph advocate and developer relations engineer at Neo4j.
As a developer relations engineer, Mark helps users embrace graph data and Neo4j, building sophisticated solutions to challenging data problems. Mark previously worked in engineering on the clustering team, helping to build the Causal Clustering feature released in Neo4j 3.1. Mark writes about his experiences of being a graphista on a popular blog at markhneedham.com. He tweets at @markhneedham.
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