Abstract: 

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.

Session Outline:
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

Bio: 

Bio Coming Soon!