Knowledge Graphs are becoming mission-critical across many industries. More recently, we are witnessing the application of Graph Data Science to Knowledge Graphs, offering powerful outcomes. But what does Graph Data Science stand for, and how does it turbocharge Knowledge Graphs?
In this talk, we will illustrate the various methods and models of Graph Data Science being applied to Knowledge Graphs and how the Neo4j platform allows you to find implicit relationships in your graph, which are impossible to detect in any other way. You will learn how centrality algorithms as well as node embeddings uniquely capture the topology of your graph and how they are being used in drug discovery as well as various other industries.
We will end the talk by showcasing how you can get started with Neo4j.
Kristof is Director Graph Data Science Technology in the Field Engineering team at Neo4j, the leading graph technology platform, where he advises on and implements graph data science solutions for Neo4j’s clients. He is also currently pursuing a PhD in Graph Machine Learning at the University of London, Birkbeck. Kristof holds a MSc in Mathematics and a MSc in Data Science from the University of London. Prior to joining Neo4j Kristof had a 20 year career in Fixed Income Sales & Trading at some of the major investment banks in London.