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Neo4j, The Leader in Graph Databases
Graph Embeddings: 5 Ways Your AI Can Learn From Your Connected Data
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 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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