Illustration: Kevin Hong // Quanta Magazine CMP722 ADVANCED COMPUTER VISION Lecture #9 – Graph Networks Aykut Erdem // Hacettepe University // Spring 2019
Illustration: StyleGAN trained on Portrait by Yuli-Ban Previously on CMP722 • image synthesis via generative models • conditional generative models • structured vs unstructured prediction • image-to-image translation • generative adversarial networks • cycle-consistent adversarial networks
Lecture overview • graph structured data • graph neural nets (GNNs) • GNNs for ”classical” network problems • Disclaimer: Much of the material and slides for this lecture were borrowed from — Yujia Li and Oriol Vinyals' tutorial on Graph Nets — Thomas Kipf’s talk on structured deep models: deep Learning on graphs and beyond 3
Deep Learning Speech data Grid games Natural language processing (NLP) Deep neural al nets s that at exp xploit: • translation equivariance (weight sharing) • hierarchical compositionality 4
Modeling Structured Data Data with Rigid Structure Unstructured Data Graph Structured Data sequences output visual data 5
Modeling Structured Data Data with Rigid Structure Unstructured Data Graph Structured Data sequences output visual data 6
Graph structured data • A lot of real-world data does not “live” on grids Social networks Knowledge graphs Citation networks Communication networks Multi-agent systems Molecules Protein interaction networks Standard deep learning architectures like CNNs and RNNs don’t work here! Road maps 7
Recipe for a good model for graphs • Handle different types of graph prediction problems Requires: Representations for graphs, nodes and edges • Handle graphs of varying sizes and structure Requires: A parametrization independent of graph size and structure • Handle arbitrary node ordering Requires: A model invariant to node permutations • Utilize graph structure Requires: A mechanism to communicate information on graphs 8
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Recap: Convolutional neural networks (on grids) Single CNN layer with 3x3 filter: (Animation by Vincent Dumoulin) 10
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