Schedule: Graph technologies and analytics sessions

The graph community has spent years defining and describing its passion: applying graph thinking to solve difficult problems. Denise Gosnell leverages years of experience shipping large-scale applications built on graph databases to share practical and tangible decisions that come into play when designing and delivering distributed graph applications. . .or playing SimCity 2000. Read more.

Talent search systems at LinkedIn strive to match the potential candidates to the hiring needs of a recruiter expressed in terms of a search query. Gungor Polatkan shares the results of the company's deployment of deep learning models on a real-world production system serving 500M+ users through LinkedIn Recruiter. Read more.

Louis DiValentin and Dillon Cullinan explain how Accenture's Cyber Security Lab built security analytics models to detect attempted lateral movement in networks by transforming enterprise-scale security data into a graph format, generating graph analytics for individual users, and building time series detection models that visualize the changing graph metrics for security operators. Read more.

The ICIJ was the team behind the Panama Papers and Paradise Papers. Pierre Romera offers a behind-the-scenes look into the ICIJ's process and explores the challenges in handling 1.4 TB of data (in many different formats)—and making it available securely to journalists all over the world. Read more.

RAPIDS is the next big step in data science, combining the ease of use of common APIs and the power and scalability of GPUs. Bartley Richardson and Joshua Patterson offer an overview of RAPIDS and and explore cuDF, cuGraph, and cuML—a trio of RAPIDS tools that enable data scientists to work with data in a familiar interface and apply graph analytics and traditional machine learning techniques. Read more.