Graph enhancements to artificial intelligence (AI) and machine learning (ML) are changing the landscape of intelligent applications.

Check out this webinar on graph algorithms’ impact on the landscape of intelligent applications.

For more videos like this one, check out upcoming and on-demand video content in the Neo4j Webinar library.

In this session, we focus on how using connected features improves the accuracy, precision and recall of machine learning models. We discuss how graph algorithms provide more predictive features and aid in feature selection that reduces overfitting.

We also look at a link prediction example that highlights how graph-based features infer collaboration with measurable improvement.

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About the Author

Mark Needham & Amy E. Hodler , Neo4j

Mark Needham & Amy E. Hodler Image

Mark Needham is a Support Engineer for Neo4j. He also blogs about software development at

Amy is the Analytics and AI Program Manager at Neo4j. She believes a thriving graph ecosystem is essential to catalyze new types of insights. Accordingly, she helps ensure Neo4j partners are successful. In her career, Amy has consistently helped teams break into new markets at startups and large companies including EDS, Microsoft, and Hewlett-Packard (HP). She most recently comes from Cray Inc., where she was the analytics and artificial intelligence market manager.Amy has a love for science and art with an extreme fascination for complexity science and graph theory. When the weather is good, you’re likely to find her cycling the passes in beautiful Eastern Washington.

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