This week we have recommendations with Personalized PageRank, Solving the bucket-filling problem, Deep Text Understanding, a new GraphQL book, Thinking in Graphs for security, and more!

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 Scott

5. This week Jeff Morris presented a webinar about Neo4j
Bloom. Jeff explains why graph visualization is such an
important tool for business users and then describes the
features that Neo4j Bloom
has to offer, such as near
natural language search and
code free graph changes.
Watch the webinar

6. Tomaz Bratanic has written a blog post explaining how to
use the Personalized PageRank algorithm. Tomaz goes
through a worked example showing how to build an article
recommender system that finds the
best articles or papers for a keyword
given the context of the researcher
asking the question.
Read the blog post

7. Vince Bickers explains how to solve the bucket-filling
problem by creating a state machine where each state is a
node, and transitions between states are relationships. He
shows how to create the state machine using various
Cypher queries and shows how to find all possible solutions
for the problem using the shortest
path algorithm.
Read the blog post

8. Michael Hunger has written a pseudo blog post in response
to a question on the Neo4j forum about Virtual Nodes and
Relationships. In the post Michael explains why they’re
useful and how they're
already being used in the
APOC and Graph Algorithms
libraries.
Read the article

9. Dinis Cruz shared the slides from his keynote, Thinking in
Graphs, at the Open Security Summit 2018. Dinis covers
various graphs in the security field, including threat
modelling, and shows some of the work
he's been doing to analyse security issues
in Neo4j.
Download the slides

10. Dr. Vlasta Kůs and Dr. Alessandro Negro have written a
blog post titled Deep text understanding combining Graph
Models, Named Entity Recognition and Word2Vec, in
which they use GraphAware NLP to build a graph
based of named entities from a
Wikipedia dataset.
Read the blog post