This Week in Neo4j: Announcing Graph Data Science 2.0 and AuraDS Releases!!!

The big news this week for Neo4j was two major releases: Graph Data Science (GDS) 2.0 and AuraDS. GDS 2.0 brings a ton of new features, such as improvements to similarity algorithms, unified machine learning pipelines, and simplified graph projections… Read more →

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New in 2022: Introducing Graph Data Science 2.0 and General Availability of AuraDS

Today, we’re celebrating the two-year anniversary Graph Data Science at Neo4j with some incredible advancements.

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This Week in Neo4j: Space Junk, SHACL, Neo4j Apps with Python, Under The Hood, Spring Data, and More

You’ve probably heard about space debris and the growing risk of collision to satellites and space stations. According to Moriba Jah, who created ASTRIAGraph at the University of Texas at Austin, there are 26,000 trackable objects in space (excluding the… Read more →

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This Week in Neo4j: ML on Graphs, Extraction Pipelines, Kubernetes, Cybersecurity, Knowledge Graph, and More Copy

There’s a wealth of fine content and varied perspectives on graphs in this week’s newsletter. The not-to-miss articles include transferring unstructured text – including emails, news articles, and more – into a knowledge graph. We also show you how to… Read more →

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Neo4j Graph Database 4.4 & Graph Data Science 1.8

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This Week in Neo4j: Viterbi Algorithm, Cypher Map Projection, Knowledge Graphs, and Full-Stack GraphQL

We have a knack for finding the power of relationships! This week, we look at the transformative influence of knowledge graphs in medical research and crime investigations. Enjoy the article demonstrating the Viterbi algorithm in Neo4j and improve your analytical… Read more →

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This Week in Neo4j: Full Stack Graph, Fraud Detection, Kafka, and Intro to Graph

Welcome to Neo4j Under The Hood! – a series of short videos, presented by our top engineering leaders. Chris Gioran, our Chief Architect, kicks off this series explaining the power of graph databases and their transformative use cases. He introduces… Read more →

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Graph Embeddings: AI That Learns from Your Data to Solve Problems

Graph embeddings learn the structure of your connected data, revealing new ways to solve your pressing problems.

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Graph Data Science Use Cases: Entity Resolution

Entity resolution, or disambiguation, is a widely applicable approach to resolve data into unique and valuable entity profiles. Without this crucial process, organizations are left making key decisions based on incomplete, misleading data.

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Graph Data Science: The Secret to Accelerating Innovation with AI/ML

Learn how graph data science helps you accelerate innovation with AI, as well as supervised and unsupervised machine learning.

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From Law Enforcement to Graph Technology: An Unlikely Relationship

After years of working in law enforcement, Dominic Teo was drawn into the realm of connected data and decided to join Neo4j. Here’s why.

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You Don’t Need a Crystal Ball When You Have Graph Data Science

Crystal balls, tarot cards, fortune tellers, Groundhog Day – all things used to make predictions. The problem is, they’re not very reliable. But you know what is reliable? Graph data science. Graph data science is a science-driven approach to gain… Read more →

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What’s New with Graph Data Science: GDS 1.7 Release

At this point, we’re all back from our summer holidays – whether we want to be or not. One consolation prize, though, is that the team at Neo4j spent our summer putting together another fabulous update to the Graph Data… Read more →

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This Week in Neo4j – Will it Graph, Python Database Backups, Knowledge Graphs, Kinesis, and Kanye West

Hello, everyone! It’s August and many of us are thinking about taking a restful break from work for the month, or perhaps returning our kids to school. However, our community members are hard at work generating some great things with… Read more →

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Neo4j Graph Data Science Library 1.6: Fastest Path to Production

Check out the new features in the Neo4j graph data science library v1.6, including improved graph embeddings and better supervised ML pipelines.

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A Year in Review: Neo4j’s Top 9 Biggest News & Announcements of 2020

2020 provided to be a year like no other – wouldn’t you agree? Nothing explains this more than supply chain woes like no toilet paper in stores! (There’s even a graph example for that!) Despite the many unique challenges this… Read more →

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Graph-native Machine Learning-Verfahren

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