Graph Neural Network-based Graph Outlier Detection: A Brief Introduction
This blog is written by Yingtong Dou, a Ph.D. candidate at the University of Illinois Chicago, working on graph mining, fraud detection, and secure machine learning. The content of this…
Machine Learning Workbench Propels TigerGraph-powered Machine Learning into the Limelight
When it was announced in May 2022, the TigerGraph Machine Learning Workbench promised to become an essential tool for data scientists who needed to harness the power of graph machine…
Major Industry Trends Driving AI and Graph
This is an abbreviated version of a presentation by Stela Solar, Global Head of Artificial Intelligence Solution Sales at Microsoft during the Fall 2021 Graph + AI Fall Summit conference.…
Artificial Intelligence at Mastercard
This is an abbreviated version of a presentation by Rohit Chauhan, Executive Vice President, AI and Security Solutions, at Mastercard, during the Fall 2021 Graph + AI Fall Summit conference.…
Improving The Treatment Of Acute Lymphoblastic Leukemia Using Graph Analytics With AI And Machine Learning
This is an abbreviated version of a presentation by Jesper Vang, Technical University of Denmark, during the Graph + AI Summit 2021 conference Our group at the Technical University of…
Building a Next Level Intelligent RASA Chatbot with TigerGraph
Did you know how easy it is to create a conversational assistant? With machine learning/Artificial intelligence. Building a powerful chatbot Assistant has always been a motivation for all kinds of…
Machine Learning Applied to Detecting Fraud in Healthcare
Fraud is a major contributing factor to escalating healthcare costs: fraud costs the U.S. about $60 billion annually and accounts for up to 10 percent of total healthcare spending. Is…