Deep Learning on Graphs

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.

圖形深度學習已成為機器學習中最熱門的話題之一。本書分為四個部分，以最佳方式滿足讀者的不同背景和閱讀目的。第一部分介紹了圖形和深度學習的基本概念；第二部分討論了從基礎到高級設置的最成熟方法；第三部分介紹了最典型的應用，包括自然語言處理、計算機視覺、數據挖掘、生物化學和醫療保健；第四部分描述了方法和應用的進展，這些進展對未來的研究具有重要性和潛力。本書是自成一體的，使更廣泛的讀者可以閱讀，包括：（1）高年級本科生和研究生；（2）希望將圖形神經網絡應用於產品和平台的從業人員和項目經理；（3）沒有計算機科學背景但希望利用圖形神經網絡推進自己學科的研究人員。

Yao Ma is a PhD student of the Department of Computer Science and Engineering at Michigan State University (MSU). He is the recipient of the Outstanding Graduate Student Award and FAST Fellowship at MSU. He has published papers in top conferences such as WSDM, ICDM, SDM, WWW, IJCAI, SIGIR and KDD, which have been cited hundreds of times. He is the leading organizer and presenter of tutorials on GNNs at AAAI'20, KDD'20 and AAAI'21, which received huge attention and wide acclaim. He has served as Program Committee Members/Reviewers in many well-known conferences and magazines such as AAAI, BigData, IJCAI, TWEB, TKDD and TPAMI.

Jiliang Tang is Assistant Professor in the Department of Computer Science and Engineering at Michigan State University. Previously, he was a research scientist in Yahoo Research. He received the 2020 SIGKDD Rising Star Award, 2020 Distinguished Withrow Research Award, 2019 NSF Career Award, the 2019 IJCAI Early Career Invited Talk and 7 best paper (runnerup) awards. He has organized top data science conferences including KDD, WSDM and SDM, and is associate editor of the TKDD journal. His research has been published in highly ranked journals and top conferences, and received more than 12,000 citations with h-index 55 and extensive media coverage.

姚馬是密歇根州立大學（MSU）計算機科學與工程系的博士生。他曾獲得MSU的優秀研究生獎和FAST獎學金。他在WSDM、ICDM、SDM、WWW、IJCAI、SIGIR和KDD等頂級會議上發表了論文，這些論文已被引用數百次。他是AAAI'20、KDD'20和AAAI'21上關於GNN的教程的主要組織者和演講者，這些教程受到了廣泛關注和好評。他曾擔任許多知名會議和雜誌（如AAAI、BigData、IJCAI、TWEB、TKDD和TPAMI）的程序委員會成員/審稿人。

Jiliang Tang是密歇根州立大學計算機科學與工程系的助理教授。之前，他是雅虎研究的研究科學家。他獲得了2020年SIGKDD新星獎、2020年傑出Withrow研究獎、2019年NSF職業生涯獎、2019年IJCAI職業生涯邀請演講和7個最佳論文（亞軍）獎。他組織了包括KDD、WSDM和SDM在內的頂級數據科學會議，並擔任TKDD期刊的副編輯。他的研究成果已發表在高排名期刊和頂級會議上，並獲得了超過12,000次引用，h指數為55，並受到了廣泛的媒體關注。