In the last week of June 2022, Neo4j Ops Manager (NOM) was released. My colleague Chris Shelmerdine published a great post with many insights into functionality but also into technical details about how NOM is built: Introducing Neo4j Ops Manager: The Tool You Need to Boost Your Ops Team’s Productivity. Read more →
Learn how to use Neo4j Graph Data Science and Python to capture key centrality and community metrics for supply chain analytics. Read more →
The Neo4j GraphQL Toolbox is a UI that allows you to easily write and execute GraphQL queries and mutations against your Neo4j database. Read more →
A wave of graph-based approaches to data science and machine learning is rising. We live in an era where the exponential growth of graph technology is predicted [1]. The ability to analyze data points through the context of their relationships… Read more →
For graph generation, we will use the singles dataset curated by Jeff Sackmann in the tennis_wta and tennis_atp repositories. Jeff’s repositories include CSV files containing all the matches on the Women’s WTA tournaments between 1920 and 2022 and the Men’s ATP tournaments from 1968 to 2022. Strictly speaking, he always keeps the repositories up-to-date. Great thanks to Jeff Sackmann for curating the datasets. Read more →
Submit Cypher queries that are shorter in length and lower on database hits while still returning the correct query results to win $27k worth of prizes. Do you love coding effectively and optimizing codes for performance? Look no further and… Read more →
With the growth of the graph category, the transition to cloud and the rise of graph data science, we are seeing more and more people and projects using the Neo4j database, developer tools, and GDS products. Historically, we have focused… Read more →
Why Graph Databases?Neo4j is the industry leader and pioneer of graph database technology and is currently the top-rated graph database management system in the world.In my experience so far with designing solutions and applications around Neo4j, I came across many materials… Read more →
How to extract actionable insights in supply chains in minutes using Neo4j GDS and Bloom to visualize operational load and regional patterns. Read more →
Learn the basic syntax of the newly released Python client for Neo4j Graph Data Science and how to get started. Read more →
This week, we’re exploring book recommendation data as a graph. And what better source for that than Goodreads?If you’d rather watch the recording of our livestream, enjoy it here and below. Otherwise, keep on reading.The DatasetI found a well-sized dataset… Read more →
Identify digital records, link them to real-world entities, and reduce duplicates using Neo4j Entity Resolution Sandbox. Read more →
In our world today, we seem to encounter crises and global struggles on a daily basis. But what if we could help others and improve life around us using the data and technology at our fingertips? Graph databases like Neo4j help us do exactly that. Read more →
A lesser-known feature of Cypher allows you to determine with equal precision what gets returned by the query in a JSON format similar to a GraphQL output. Read more →
In this blog post, we’ll use Neo4j to turn the European Gas Network into a knowledge graph to analyze the data.Photo by Rostislav Artov, UnsplashThe crisis between Ukraine and Russia caused relations between Russia and the E.U. to fall to the lowest… Read more →
Exploring the meaning of data with an appropriate visualization is extremely important for us, and for the users who want to explore the relationships and properties stored in their database. Read more →
With winter officially behind us, we at Neo4j have been busy in the workshop sharpening our (Dev)Tools with meticulous precision to bring you the latest releases. Read more →
We’re heavy users of Auth0 at Neo4j, with many of our products and services using Auth0 for authentication. For example, when logging into Neo4j Aura or Neo4j Sandbox, you are greeted with a customized Auth0 login screen. Read more →
News is much more than just the articles you see on the front pages. A lot of the detail is hidden behind metadata that’s not immediately visible. Today we want to look behind the scenes, using the NYTimes API to access some of the article metadata. Read more →
Cryptocurrencies have become more and more integrated into our daily life. Many cryptocurrencies like Bitcoin and Ethereum are decentralized networks based on blockchain technology. They have a wide array of use-cases and have been implemented by various companies and governments.… Read more →
In part 4 of our fraud detection series, we will cover how to apply graph machine learning to predict the high fraud risk user accounts we labeled in parts 1, 2, and 3. Read more →
In part 3 of our fraud detection series, we may want to expand beyond our business logic to automatically identify other users that are suspiciously similar to the fraud risks already identified. Read more →
In part 2 of this fraud detection series, we will provide more formal definitions for resolving entities that will allow us to partition well-defined communities in a scalable manner. Read more →
In the first part of this fraud detection series, we will introduce the sample graph dataset we are using and begin exploring the graph for potential fraud patterns. Read more →
Fraud Detection is one of today's most challenging data science problems. Thankfully, Neo4j Graph Data Science (GDS) offers practical solutions that empower data scientists to make rapid progress in fraud detection analytics and machine learning. Read more →
