Graph Data Science - Improve predictions using connections in data

[masked]: Networking & Food[masked]: Main Talk - Amy Hodler, Neo4j

Graph Data Science - Improve predictions using connections in data

The world is naturally connected and relationships are the strongest predictors of behavior. In this talk, you’ll learn how graphs (mathematical representations of connected data) can be used to improve predictions by taking advantage of relationships and network structures. We’ll walk through the steps of Graph Data Science employed alongside machine learning and AI systems including knowledge graphs, graph analytics and graph feature engineering. We’ll also review a link prediction workflow using graphs to increase ML model accuracy.

Amy Hodler is a network science enthusiast and program director for AI and graph analytics at Neo4j. Amy is the co-author of the O’Reilly book, Graph Algorithms: Practical Examples in Apache Spark and Neo4j. She tweets @amyhodler

Get the book from:
https://neo4j.com/blog/new-oreilly-book-graph-algorithms-spark-neo4j/