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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.