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Representation Learning on Graphs

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  • UserJure Leskovec - Associate Professor of Computer Science at Stanford University, Chief Scientist at Pinterest, and investigator at Chan Zuckerberg Biohub
  • ClockWednesday 20 March 2019, 16:15-17:00
  • HouseLecture Theatre 2, Computer Laboratory.

If you have a question about this talk, please contact jo de bono .

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. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including random-walk based algorithms, and graph convolutional networks. We will discuss applications to web-scale recommender systems, healthcare and knowledge representation and reasoning.

The slides from the talk: http://i.stanford.edu/~jure/pub/talks2/graphsage_gin-cambridge-mar19.pdf

The talk was not recorded due to technical issues.

This talk is part of the Wednesday Seminars - Department of Computer Science and Technology series.

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