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 development of graph-learning benchmarks as well as open research problems.
Monday 24 at 18:45