Relationships are highly predictive of behavior, yet most data science models overlook this information because it's difficult to extract network structure for use in machine learning (ML).

With graphs, relationships are embedded in the data itself, making it practical to add these predictive capabilities to your existing practices.

That’s why we’re presenting and demoing the use of graph-native ML to make breakthrough predictions. This will cover:

  • Different approaches to graph feature engineering, from queries and algorithms to embeddings.
  • How ML techniques leverage everything from classical network science to deep learning and graph convolutional neural networks.
  • How to generate representations of your graph using graph embeddings, create ML models for link prediction or node classification, and apply these models to add missing information to an existing graph/incoming data.
  • Why no-code visualization and prototyping is important.

Join us to learn hands-on!

Instructor's Bio

Dr. Alicia Frame

Director of Product Management, Graph Data Science, Neo4j

Alicia Frame is the lead product manager for data science at Neo4j. She's spent the last year translating input from customers, early adopters, and the community into the first truly enterprise product for doing data science with graphs: Neo4j's Graph Data Science Library. She has a phd in computational biology from UNC Chapel Hill, and her background is in data science applications in healthcare and life sciences.She's worked in academia, government, and the private sector to leverage graph techniques for drug discovery, molecular optimization, and risk assessments -- and is super excited to be making it possible for anyone to use advanced graph techniques with Neo4j.


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    ON-DEMAND WEBINAR: Relationships Matter: Using Connected Data for Better Machine Learning

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