Combating Money Laundering: Graph Tech Fights Serious Crimes

Read the first blog in Neo4j’s new six-part series on how Neo4j’s graph technology can help you combat money laundering.

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The Power of Graph Data Science: 5-Minute Interview with Alicia Frame

Check out this week’s 5-Minute Interview with Alicia Frame, Senior Data Scientist at Neo4j, about the Neo4j Graph Data Science Library.

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Building the Enterprise Knowledge Graph

Learn how to build and implement an enterprise knowledge graph from the groun up using the Google Drive API and Neo4j Python.

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Your World Seen Through Your Eyes with Neo4j Bloom

Andreas Kolleger discusses Neo4j Bloom and the possibilities it opens up for enterprise organizations across their departments.

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Graph-Powered Recommendations: How Smart Recommendations Drive Value

Read blog one in Neo4j’s five-part series on how graph-powered recommendation engines drive value for enterprise businesses.

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Financial Fraud Detection with Graph Data Science: Identifying Fraud Rings

Read blog four in this series on how financial services enterprises are using Neo4j’s graph technology to prevent and detect financial fraud.

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How Previa Is Reimagining Healthcare

Editor’s Note: This presentation was given by Alessandro Svensson and Sarah Johnnesson at GraphConnect New York in September 2018. Presentation Summary Alessandro Svensson is the Head of Neo4j Innovation Labs within the U.S. He and Sarah Johnnesson of Previa discuss… Read more →

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Financial Fraud Detection with Graph Data Science: Analytics and Feature Engineering

Read blog two on how financial services enterprises are using Neo4j’s graph technology to prevent and detect financial fraud.

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Announcing Neo4j for Graph Data Science

Discover the benefits of the Neo4j for Graph Data Science, the first data science environment built to harness the predictive power of relationships.

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Fujitsu Laboratories of America Partners with Larus to Provide Enhanced Graph AI Technologies to Neo4j Customers

Fujitsu Laboratories is building on Larus’s expertise in Neo4j customer requirements and application domain knowledge with advanced graph AI technologies.

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Machine Learning Algorithms

Lauren Shin, Neo4j Developer Relations Intern, introduces machine learning and offers three approaches to better analyze ML data.

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#GraphCast: Responsible AI

This week’s #GraphCast features Responsible AI where Amy Hodler discusses ethical development and application of artificial intelligence and technology.

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GraphConnect 2020 Agenda: Everything You Need to Know

The health and safety of our community, partners and employees are of the utmost importance to Neo4j. After monitoring the COVID-19 situation closely, we have decided to postpone GraphConnect 2020 in New York City to 2021. We are actively scoping… Read more →

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Ontologies in Neo4j: Semantics and Knowledge Graphs

Jesús Barrasa presents the two biggest uses of ontologies in graphs using Neo4j and NeoSemantics; inferences and interoperability.

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Connecting Healthcare

Learn about connecting healthcare data, including its challenges and how graph databases might be able to help solve healthcare’s wicked problems.

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Graphs to Fight Diabetes

Discover how Neo4j graph databases contribute to the study, understanding, prevention and treatment concerning diabetes with Alexander Jarasch from DZD.

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The Top 7 Neo4j Videos of 2019

In lieu of our bi-weekly #GraphCast, we thought we would share some of the best Neo4j videos from 2019. Consider this an almost-New Year’s Eve celebration of all things video. (No order or favorites here. Just cool videos in a… Read more →

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Graphs on GPUs: 5-Minute Interview with Saul Rosales

In this week’s five-minute interview (conducted at GraphTour DC 2019), we discuss how Saul Rosales and his team use Neo4j with Graphistry.

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Fullstack GraphQL with Neo4j

Dive into this introduction to GraphQL with Will Lyon. Built by Facebook, GraphQL is a query language to describe data sets and deliver predictable results.

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Real Examples of Why We Need Context for Responsible AI

Read part one of this two-part series on responsible AI. Discover real-world examples of the myths,., biases and more that currently surround AI.

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