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Deep learning (particularly deep convolutional neural networks) have revolutionized the field of data-driven modeling techniques. Through an increase in data coverage, computational horsepower, and model complexity we have regularly seen records set and broken in computer vision.
One fundamental limitation of deep convolutional networks is the data they apply to. A “convolution” is in essence a dense matrix-matrix multiply. It operates on dense, regularly structured data (e.g. a digital image with RGB values for every pixel in a regular grid).
This presentation offers an overview of graph convolutional networks (GCNs) – an extension to classical convolutional networks for irregular data. It includes an overview of GCNs and a walkthrough of two example applications of GCNs (one unsupervised and one semi-supervised) on a real world streaming dataset from the GDELT Project.
