Graph enhancements to AI and ML are changing the landscape of intelligent applications. In this session, we’ll focus on how using connected features can help improve the accuracy, precision, and recall of machine learning models. You’ll learn how graph algorithms can provide more predictive features as well as aid in feature selection to reduce overfitting.  We’ll look at a link prediction example to predict collaboration with measurable improvement when including graph-based features.

Amy Hodler
Graph Analytics & AI Program Manager, Neo4j

Amy is the Graph Analytics & AI program manager at Neo4j. She loves seeing how the community uses graph analytics to reveal structures within real-world networks and infer behavior. Amy is the co-author of the O'Reilly book, "Applied Graph Algorithms in Apache Spark and Neo4j". 


Mark Needham
Developer Relations Engineer, Neo4j

As a developer relations engineer, Mark helps users embrace graph data and Neo4j, building sophisticated solutions to challenging data problems. He is the co-author of the O'Reilly book, "Applied Graph Algorithms in Apache Spark and Neo4j" published in spring 2019.
Kelsey Bieri
Data Governance Analyst, ICC

Kelsey Bieri is a Data Governance Analyst at ICC in the Master Data Management and Data Governance Practice. She has contributed to numerous data governance and data lineage projects in the Banking industry, helping organizations build a better understanding of their data universe. Kelsey holds a degree in Management Information Systems from the College of Business at Ohio University.

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