Lecturer: Bryan Perozzi is a Research Scientist in Google Research’s Algorithms and Optimization group, where he routinely analyzes some of the world’s largest (and perhaps most interesting) graphs. Bryan’s research focuses on developing techniques for learning expressive representations of relational data with neural networks. These scalable algorithms are useful for prediction tasks (classification/regression), pattern discovery, and anomaly detection in large networked data sets. Bryan is an author of 30+ peer-reviewed papers at leading conferences in machine learning and data mining (such as NeurIPS, ICML, KDD, and WWW). His doctoral work on learning network representations was awarded the prestigious SIGKDD Dissertation Award. Bryan received his Ph.D. in Computer Science from Stony Brook University in 2016, and his M.S. from the Johns Hopkins University in 2011.
Abstract: How can neural networks best model data which doesn’t have a fixed structure? In this talk, I will discuss graph neural networks (GNNs), a very active area of current research in machine learning aimed at answering this interesting (and practical) question. After reviewing the basics of GNNs, I’ll discuss some challenges applying these methods in industry, and some of the methods we’ve developed for addressing these challenges.
Graph Mining with Graph Neural Networks – Recording
Graph-Mining-with-Graph-Neural-Networks-Granada-DaSCI-Talk-2021