Graphs can represent almost any kind of data, from complex supply chains, medical research, customer 360, and fraud detection.

Implemented in production-grade within the Neo4j Graph Data Science library, Graph Embeddings are an advanced AI technology used to translate your connected data – knowledge graphs, customer journeys, and transaction networks – into a predictive signal.

Applications of Graph Embeddings are numerous: finding fraud, entity resolution and disambiguation, improving product recommendations, discovering new drugs and predicting churn.

This workshop will help you:


  • Make the most of Graph Embeddings
  • Understand how to train high-performing supervised machine learning models to perform tasks like node classification and link prediction.
  • Answer questions within your connected data, analyzing 5 different use cases



Nicolas Rouyer
Pre Sales Engineer

Nicolas Rouyer is Senior Pre-Sales at Neo4j.   Previously Big Data Expert and Senior Architect at Orange - a telco provider with additional activities in System Integration as well as in Finance/Banking - he worked for the past 10 years on various projects including Neo4j. Before his time at Orange, he spent a number of years as a software engineer and Consultant for SIs such as Sopra. Co-author of the book Neo4j - A Graph Project Story and 2 other books in French, Nicolas has a Master degree in Computer Sciences from the University of Nancy in France. In his spare time, he enjoys running, playing tennis and violin.




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