Transcription of Graph Representation Learning
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Graph Representation Learning
Graph Representation LearningWilliam L. HamiltonMcGill University2020 Pre-publication draft of a book to be published byMorgan & Claypool version released with relevant copyrights held by the author andpublisher extend to this pre-publication : William L. Hamilton. (2020). Graph Representation Lectures on Artificial Intelligence and Machine Learning , Vol. 14,No. 3 , Pages data is ubiquitous throughout the natural and social sciences,from telecommunication networks to quantum chemistry. Building relational inductivebiases into deep Learning architectures is crucial if we want systems that can learn,reason, and generalize from this kind of data. Recent years have seen a surge in researchon Graph Representation Learning , including techniques for deep Graph embeddings,generalizations of convolutional neural networks to Graph -structured data, and neuralmessage-passing approaches inspired by belief propagation.
I also owe a great debt of gratitude to the students of my winter 2020 gradu-ate seminar at McGill University. These students were the early \beta testers" of this material, and this book would not exist without their feedback and en-couragement. In a similar vein, the exceptionally detailed feedback provided by
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