Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. In this talk I will discuss recent advancements in the field of Graph Neural Networks that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning. I will provide a conceptual overview of key advancements in this area of representation learning on graphs, including graph convolutional networks and their representational power. We will also discuss applications to web-scale recommender systems, healthcare, and knowledge representation and reasoning.
Jure Leskovec is Associate Professor of Computer Science at Stanford University, Chief Scientist at Pinterest, and investigator at Chan Zuckerberg Biohub. His research focuses on machine learning and data mining with graphs, a general language for describing social, technological and biological systems. Computation over massive data is at the heart of his research and has applications in computer science, social sciences, marketing, and biomedicine. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper and test of time awards. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, PhD in machine learning from Carnegie Mellon University and postdoctoral training at Cornell University.