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Deep Learning for Relational Data: Graph Neural Networks and Beyond

Deep Learning for Relational Data: Graph Neural Networks and Beyond


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