Happy New Year everybody, and welcome to our first version of TWIN4j of 2019. I’d only just got used to writing the year as 2018! This is traditionally a quiet week, but we’ve still got some good stuff for you to read and listen to. I always enjoy Joe Depeau’s webinars and he’s product another one showing how to use Neo4j for Anti-Money laundering. David Mack gives us a crash course in machine learning on graphs, Will Lyon replicates GitHub’s GraphQL API, and Dan McCreary looks forward to 2019 in the world of graphs.

1. This Week in Neo4j

2. Developer Relations Engineer at Neo4j
2

3. The week's graph database news
in one handy slide deck!
Find the archive at
neo4j.com/tag/twin4j

4. Learn more about Tim

5. Joe Depeau presented a webinar showing how to use
Neo4j to help with anti money laundering investigations.
Joe gives an overview of money laundering and anti money
regulations, before describing some of the common
problems encountered. He then demos
an investigation of a fictitious bank using
Neo4j and the Bloom visualization tool.
Watch the video

6. Will Lyon has written a post showing how to
build your own version of GitHub’s GraphQL
API using neo4j-graphql.js and data exported
via the download-all-of-your-GitHub-data feature. Will
shows how to import the raw data into Neo4j, then builds
GraphQL type definitions and auto generates a GraphQL
schema, before exploring the data with GraphQL queries.
Read the blog post

7. Will was also interviewed by the GraphQL Patterns
podcast.
They discuss what Neo4j is, how it works, how the
database integrates with GraphQL, and
how to get started with the technologies.
Listen to the podcast

8. David Mack has written a blog post in which he shares
resources and approaches to get started with machine
learning on graphs. David gives an overview of the field,
explains the common tasks in Graph ML and how we might
go about solving them, and concludes with a long list
of further reading to keep you busy into the new
year!
Read the blog post

9. Dan McCreary has written an excellent article in which he
summarises the world of graphs in 2018, and looks forward
to the coming year. The post covers Knowledge Graphs,
Google DeepMind’s paper on Graph ML, Custom Graph
Hardware, and more. If you want a crash course in the
state of graphs at the start of 2019, this
is a good place to start.
Read the blog post