Graph Embeddings: 5 Ways Your AI Can Learn From Your Connected Data

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!

Open Data Science

 

 

 

Open Data Science
One Broadway
Cambridge, MA 02142
info@odsc.com

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Youtube
Consent to display content from - Youtube
Vimeo
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google