Hi graph gang,
In this week’s video from the NODES 2019 conference, Joe Depeau explains how graph algorithms through the medium of classic teen films.
Nathan Smith tries to solve a Sudoku, Jesús Barrasa graphs Christmas messages, and Mike Solomon analyses Twitter data.
And finally, Greg Shackles uses F# and Neo4j to analyse a .NET dependency graph.
Enjoy! Mark Needham, Karin Wolok, and the Developer Relations team
Featured Community Member: Michael Porter
This week’s featured Neo4j Community member is Michael Porter, Founder at Muddy Boots Code.
Michael Porter – This Week’s Featured Community Member
Michael is a blogger, a speaker, a certified developer, and a Neo4j Ninja.
Did we mention he also has his own company? He’s a software developer consultant specializing in oil and gas. What’s special about Michael’s contributions is that he isn’t just doing it to ‘do it’. He’s doing it because he’s passionate about helping people.
We recently launched the Neo4j Ninja program and Michael managed to reach #1 place on our leaderboard!
Congrats, Michael, and thanks again for all that you do!
NODES 2019: Making Graph Algorithms ‘Clique’
In this week’s video from the NODES 2019, Joe Depeau explains how graph algorithms through the medium of classic teen films.
After giving an overview of the graph algorithm library, Joe moves on to some applied examples. We use PageRank to find the popular students in the 1988 movie Heathers and Label Propagation to find communities of people in Mean Girls.
Coloring a Sudoku graph with Neo4j
Nathan Smith tries using the recently released K-1 Coloring Graph Algorithm to colour a Sudoku Graph.
In the post Nathan shows how to model a Sudoku grid in Neo4j, and then tries to fill in a valid configuration using the algorithm. It gets close, but doesn’t quite solve the puzzle perfectly!
TriGraph, Guardian Top 100 Male Footballers, Analysing Twitter Engagement data
- Daniel Wilms used Neo4j to analyse data from the Ironman Vega World Championships 2019, an endurance event where participants swim 2.4 miles, bike 112 miles, and run 26.2 miles.
- I wrote a QuickGraph blog post in which I showed how to analyse The Guardian’s Top 100 Male Footballers of 2019 dataset using Neo4j.
- Mike Solomon published some thoughts on how to store and analyze Twitter engagement data using Neo4j.
- Ben Albritton shared a link to a Neo4j database containing the Islamic Scientific Manuscripts Initiative, an initiative led by Dr. Sally Ragep.
- Michael Porter shows how easy is it to evolve a graph model via a GraphQL schema using the GRANDstack.
QuickGraph: Christmas Messages Graph
In Jesús Barrasa‘s latest QuickGraph blog post, he analyses the Christmas messages from some of Europe’s heads of state.
Jesús uses the popular NLTK library to process the speeches, and then creates a graph from the extracted words and word stems.
He then queries the graph, finding out which countries are most self referential, and which mention climate the most. Jesús concludes the post by using the Graph Algorithms Library to compute the similarity of the Christmas messages.
Analyzing .NET Dependencies with Neo4j
Greg Shackles uses a combination of F# and Neo4j to explore the dependencies between libraries in a .NET project.
Greg shows how to export the dependencies to a CSV file using an F# script, load them into Neo4j using LOAD CSV, and explore the dependency graph with various Cypher queries.
F# was one of my favourite scripting languages when I used iit at the start of the last decade, so it was great to see it being used alongside my favourite graph database.
Tweet of the Week
My favourite tweet this week was by Michael Simons:
Yesterday, @tinasimons and the kid made cookies with spices from @etc_aachen's Christmas card using a cookie cutter designed by my @neo4j colleaque Louise Söderström… That's something 🙂 pic.twitter.com/Jrc7FKsBFy— Michael Simons (@rotnroll666) January 9, 2020