Full Session Description

Graph algorithms such as PageRank, community detection, and similarity match have moved from the classroom to the toolkits of both data scientists and business analysts. Organizations are gaining actionable insights and supercharging their AI by interconnecting and analyzing their data. Users don’t need to be computer scientists or programmers to derive meaningful benefits. Increasingly, graph databases come with graph algorithm libraries. Users only need to understand first, what each type of algorithm is designed to tell them, and next, what makes one algorithm different from another.This presentation will systematically describe and illustrate five categories of graph algorithms. We will also dive into how each of these algorithms has been used — individually, in combinations, and for ML feature extraction — to answer real business challenges in key verticals such as banking, financial services, healthcare, pharmaceutical, internet, telecom and eCommerce. We will also discuss the computational requirements for algorithms, to help attendees evaluate and select the right platform.

Dr. Victor Lee

Head of Product Strategy & Developer Relations @ TigerGraph

About the author

Dr. Victor Lee is Head of Product Strategy and Developer Relations at TigerGraph. He brings a strong academic background, decades of industry experience, and a commitment to quality and service. Victor was a circuit designer and technology transfer manager at Rambus, before returning to school for his computer science PhD, focusing on graph data mining. He received his BS in Electrical Engineering and Computer Science from UC Berkeley, MS in Electrical Engineering from Stanford University, and PhD in Computer Science from Kent State University. Before TigerGraph he was a visiting professor at John Carroll University.