GraphConnect 2018: Predictive Analysis from Massive Knowledge Graphs on Neo4j
Abstract
Prof. David Bader, one of the nation’s leading experts in massive-scale graph analytics, presents a Neo4j case study on predictive analytics on a homeland security knowledge graph that connects disparate data from multiple sources such as spreadsheets and relational databases. Graphs are a natural representation for connecting information in real-world challenges such as understanding financial transactions in digital currencies, finding new communities in social networks, increasing power grid resiliency, and protecting us from cyberattack. Bader will discuss his Spatio-Temporal Interaction Networks and Graphs (STING) initiative that supports new methods for finding interesting patterns and features in these critical knowledge graphs.
GraphConnect is the global conference for the graph technology community. Drawing experts and enthusiasts alike, the gathering focuses on Neo4j, the world’s leading graph database and the #1 platform for connected data.
Making its third appearance in New York, GraphConnect welcomed a gathering of graph database developers, architects and CTOs all under one roof at the Marriott Marquis Times Square in Manhattan. Attendees were from many industries, including financial services, manufacturing, retail, healthcare and more.
Speakers from around the world presented their impact stories on how Neo4j made a difference in terms of innovation, technology and the bottom line — from the scale of small startups all the way up to global enterprises.
David A. Bader
Distinguished Professor and Director of the Institute for Data Science
David A. Bader is a Distinguished Professor in the Department of Computer Science at New Jersey Institute of Technology.