Deep learning on graphs has become one of the hottest topics in
machine learning. The book consists of four parts to best
accommodate our readers with diverse backgrounds and purposes of
reading. Part 1 introduces basic concepts of graphs and deep
learning; Part 2 discusses the most established methods from the
basic to advanced settings; Part 3 presents the most typical
applications including natural language processing, computer
vision, data mining, biochemistry and healthcare; and Part 4
describes advances of methods and applications that tend to be
important and promising for future research. The book is
self-contained, making it accessible to a broader range of readers
including (1) senior undergraduate and graduate students; (2)
practitioners and project managers who want to adopt graph neural
networks into their products and platforms; and (3) researchers
without a computer science background who want to use graph neural
networks to advance their disciplines.
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