Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Network effects: Working with modern graph analytic systems

Zachary Hanif (Capital One)
5:25pm–6:05pm Wednesday, 09/12/2018
Data science and machine learning
Location: 1A 06/07 Level: Intermediate
Secondary topics:  Financial Services
Average rating: ****.
(4.67, 3 ratings)

Who is this presentation for?

  • Technical practitioners interested in graph analytics

Prerequisite knowledge

  • A basic understanding of Python or Java

What you'll learn

  • Explore graph analytic technologies and techniques

Description

Graphs are everywhere, and a firm understanding of graph-based analytical techniques can be extremely powerful when applied to modern practical problems. Modern frameworks and analytical techniques are making graph analysis methods viable for increasingly large workloads and complex tasks. As components of a machine learning toolkit, graph analytics have a number of advantages, not least of which are their interpretability, robustness to imbalanced data, and modeling flexibility.

Zachary Hanif examines three prominent graph analytic methods, including graph convolutional networks, and applies them to concrete use cases. Zachary details where these methods fit into existing machine learning pipelines as well as areas where graph analysis provides compelling alternatives to less interpretable models. Along the way, he discusses traversal, vertex-based, and deep learning methods for exploring graph networks and demonstrates ideal problem statements for each method. Zachary pays particular attention to graph convolutional networks, a cutting-edge deep learning methodology for analyzing graph data. He concludes by examining applications of these techniques for multiple modern workloads and contexts.

Photo of Zachary  Hanif

Zachary Hanif

Capital One

Zachary Hanif is a director in Capital One’s Center for Machine Learning, where he leads teams focused on applying machine learning to cybersecurity and financial crime. His research interests include applications of machine learning and graph mining within the realm of massive security data and the automation of model validation and governance. Zachary graduated from the Georgia Institute of Technology.