Welcome to This Week in Neo4j, where we round up the last week in the world of graph databases. This week our Chief Scientist Dr Jim Webber describes how to run Neo4j in a Multi Data Center Environment, Max De Marzi shows us how to find the shortest path on a rail network, and Stefan Bieliauskas shows us why graphs are a perfect fit for modeling data provenance

1. This Week in Neo4j
16th February 2019

2. Mark Needham
Developer Relations Engineer at Neo4j
mark.needham@neo4j.com / devrel@neo4j.com
@markhneedham
2

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

4. Featured Community: David Stevens
Learn more about David

5. Jim Webber was back in the video studio, this time
recording a video explaining how to run Neo4j in Multi
Data Center Environments. Jim describes
how to configure Neo4j servers with
metadata to optimise the way that data is
both queried and moved between them.
Running Neo4j in Multi Data Center Environments
Watch the video

6. Tomaz Bratanic shows how to use the Pearson Similarity
algorithm to make sense of Kaggle’s Young People Survey
dataset. This dataset contains music preferences, phobias,
and health habits, and Tomaz
shows how to use the algorithm
to work out correlations between
the answers in these different categories.
Community detection of survey responses based on
Pearson correlation coefficient with Neo4j
Read the blog post

7. Versioning graphs is a commonly asked question, and Tom
Geudens has started a series of post explaining the
different approaches. Installment 1 focuses
on time-based versioning of graphs. Using an
e-commerce example, Tom shows how to
separate identity from state, where the name of
shops and the products that they sell can vary over time.
Read the blog post
Graph versioning Episode one — Time Based

8. Jennifer Reif‘s Marvel Series is back, and this week Jennifer
shows us how to build the controller and service classes for
handling requests and shaping results. Everything is now in
place to feed the data into d3 to better visualize the Marvel
Universe in the next
installment!
Create a Data Marvel — Part 8: Controlling and
Servicing our Comic Endpoints
Read the blog post

9. In his latest blog post, Max De Marzi explores a rail road
dataset, showing how to load the data and make sense of it
using shortest path algorithms. He also takes us through
the code to write a procedure
to find the shortest path based
on total distance rather than
number of cities.
Calculating the best rail road paths in Neo4j
Read the blog post

10. If you liked this check
out the blog post
This Week in Neo4j - 16th February 2019