Learning with Graphs

Gonzalo Mateos, Ph.D.
Associate Professor
University of Rochester, Rochester, NY

Date:
02/23/2024

Time:
11:00 am – 12:00 pm CST

Location:
San Pedro 1, Yotta Room 430
506 Dolorosa St, San Antonio, TX 78204

Zoom: https://utsa.zoom.us/j/94807623288

Abstract:
This talk is broadly about learning from network data, which arises for instance with applications involving online social media, recommendation systems, transportation, and network neuroscience. By fruitfully exploiting the inductive biases in relational data, graph neural networks (GNNs) have attained unprecedented performance in various machine learning tasks, including node/graph classification, link prediction, and graph generation. To provide additional motivation, I will start with a user-friendly and didactic introduction to graph signal processing. The goal is to establish the foundations and basic concepts that will be useful to introduce graph GNNs in an intuitive way. After discussing architectures and key properties that make GNNs the model of choice when it comes to learning from relational data, I will highlight several success stories of GNN-based learning for Amazon’s recommendation system, Google Maps navigation, antibiotic discovery, and our own work on explainable brain age prediction.