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.

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 Coming Soon!

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

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