Deep Learning on Graphs

author: Jure Leskovec, Computer Science Department, Stanford University
published: Jan. 17, 2019,   recorded: November 2018,   views: 2074


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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.

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Comment1 Frank Rager, December 30, 2020 at 2:51 p.m.:

Very interesting video! I was looking for information about machine learning. I need this material to get prepared for my college application. Also, I like to read such inspirational articles as to become motivated.

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