Hello, everyone!
? Is it just me or is this month getting spookier and spookier by the day? I’m sure it’s just me, but it still doesn’t feel like I’m the only one who’s been spooked this month. At least not if you’ve been working at Twitch.
? Twitch made an “oopsie” and now their source code is all over the internet. While we didn’t partake in this “oopsie,” we’ve been working on a new Twitch dataset you can use to analyze streamer data in this issue.
⚠️ There is also a new Stack Overflow dataset to explore this week.
? For those of you who like beer, we’ve got a beer feature for you in this issue, where we demonstrate how a certain individual makes his own beer recommendation engine. If that isn’t a great use of graphs then I don’t know what is.
Furthermore, we feature Graphaule – a podcast/article series that focuses on graph value and how to bring these concepts together for value creation. We also feature some hands-on experiences within health care and life science, as well as a masterclass in knowledge graphs.
Another cool thing is that you can still get a t-shirt for free when you certify as a Neo4j Certified Professional.
That’s it for me! Have a great week and see you next week.
Cheers ? , Max Andersson
Featured Community Member: Håkan Löfqvist
This week’s featured community member is Håkan Löfqvist.
Håkan Löfqvist – This Week’s Featured Community Member
This week’s featured member is Håkan Löfqvist, who works as a Field Engineer at Neo4j out of Stockholm, Sweden, helping many of our customers find success in their Neo4j projects.
Even before joining Neo4j in 2020, Håkan had a long history with Neo4j (since 2013!), having previously worked as the CTO of Monocl. Moncol uses Neo4j together with Spark and ElasticSearch to build, find, and prioritize relationships for medical use cases.
What is really amazing about Håkan is his involvement in our user community. He has answered hundreds of questions both in the chat on Discord and in our Forums on all topics, from Cypher and GDS to deployments and configuration.
He has also contributed to APOC, given talks, and written articles.
Thanks so much for all your help Håkan. Please keep it coming.
Connect with Håkan on LinkedIn:
? Beer Recommendation Engine
I don’t know about you, but I occasionally indulge in a beer after work. Bart Simons decided to take a break from drinking beer to scrape the beer tracking app Untapped, in order to create his very own beer recommendation engine. Cheers!
Bart Simons is a data scientist at a major consultancy firm. He created a beer recommendation engine using data from the Untappd app. He scraped data about people’s preferences, social networks, and visits to venues. Using the data, he created a graph database and created a recommendation function. He then created a score based on the similarity of beer users’ ratings. The project is a work in progress – he’s putting the scraper in a script form instead of a command-line.
Visualizing Similarities Between Companies
Khuyen Tran uses Neo4j to find the similarities between artificial intelligence companies. The data is being made available with the public domain CC0 license with the help of Diffbot’s knowledge graph. The graph is then used to find the most similar companies.
? Twitch Sandbox
Twitch is a streaming service that allows users to watch live video feeds and chat with other users. Mainly focusing on gaming, their platform is for sharing content of all kinds. In this dataset, you can explore the data that was originally scraped from Twitch. Using the data, you can explore the streamers and their streams, what games they’re playing, and how they are interacting with each other.
Play with the new Twitch dataset in Neo4j Sandbox, and remember to tag your findings with #neo4j on Twitter.
⚠️ Stack Overflow Dataset
If you ever deal with code – no matter how small – you’ve probably seen Stack Overflow. It’s a website that allows users to ask questions and answer them. Discover Questions, Answers, Comments, and Tags from Stack Overflow in a graph database.
Remember to tag your findings with #neo4j on Twitter.
Graphalue
Stefan Wendin and Rik Van Bruggen are going on a quest together to better understand the value of graphs. The destination is “Graphalue” (pronounced grɑːfæljuː or grAfAl-yoo), which is a reference to the mythical land that brings the value concept together with the technical concepts of graphs. This quest is not going to be too long, but not too short either. They’ll be thinking this through, together, and creating a number of articles and podcasts about the topic on the way.
Masterclass of the Knowledge Graphs
The goal of this masterclass is to take participants from raw, unstructured text into the creation of a full knowledge graph. Participants will learn how to use basic natural language processing (NLP) to construct a small knowledge graph using Python and a graph database. From here, we will explore techniques around creating graph embeddings as the entry point for binary classification problems for node classification. Queries will be demonstrated using the Cypher Query Language, both from within the Neo4j web browser as well as from Python in the notebook environment.
Held by our very own Clair Sullivan.
Neo4j Health Care and Life Sciences Workshop
Are you working in the health care or life science industry? If so, perhaps you’ve heard that graph technology can make your life easier! In this series of workshops, we will explore the power of graph technology in the health care and life sciences industries and showcase practical applications for common problems.
Tweet of the Week
My favorite tweet this week was by mesirii:
Loading 1000 top US cities into #neo4j
— Michael Hunger (@mesirii) October 14, 2021
CALL apoc.load.jsonArray("https://t.co/DBhcn2915Z")
YIELD value as v
CREATE (c:City {name:v. city, loc:point({latitude:v.latitude, longitude:v.longitude}), population: toInteger(v.population), rank: v.rank, state:v.state})
Don’t forget to RT if you liked it too!
