Relationships Matter: Using Connected Data for Better Machine Learning


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. This tech talk will cover different approaches from graph feature engineering, from queries and algorithms to embeddings, and how to generate representations of graph using procedures provided in Neo4j Graph Data Science package to generate graph embeddings, ML models for link prediction or node classification, and apply these models to add missing information to existing data.


Fanghua (Joshua) Yu joined Neo4j since late 2017, and is now leading the sales engineer team for APAC region. During Joshua’s 20 years of career in IT, he has taken various roles as a developer, database designer, technical lead, consultant and solution architect. He also has extensive experience in banking and financial industry for over 11 year, with expertise in data and analytics, core banking systems, payments, application and integration architecture.
Joshua has a PhD degree in Computer related subject, and now lives in Melbourne Australia.

Open Data Science




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