Foundations of Data Science - Representation and Learning in Graph Neural Networks

Stefanie Jegelka (MIT) Title: Representation and Learning in Graph Neural Networks Abstract: Graph Neural Networks (GNNs) have become a popular tool for learning representations of graph-structured inputs, with applications in computational chemistry, recommendation, pharmacy, reasoning, and many other areas. This talk will show recent results on representational power and learning in GNNs. First, we will address representational power and important limitations of popular message passing networks and of