Graph representation learning has recently become one of the hottest topics in machine learning.
One particular instance, graph neural networks, is being used in a broad spectrum of applications ranging from 3D computer vision and graphics to high energy physics and drug design.
Despite the promise and a series of success stories of graph deep learning methods, we have not witnessed so far anything close to the smashing success convolutional networks have had in computer vision.
In this talk, I will outline my views on the possible reasons and how the field could progress in the next few years.
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