Deep Learning on Graphs
暫譯: 圖上的深度學習

Yao Ma, Jiliang Tang

Deep Learning on Graphs

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商品描述

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.

商品描述(中文翻譯)

深度學習在圖形上的應用已成為機器學習中最熱門的主題之一。本書分為四個部分，以最佳方式滿足我們背景和閱讀目的各異的讀者。第一部分介紹圖形和深度學習的基本概念；第二部分討論從基本到進階設置的最成熟方法；第三部分展示包括自然語言處理、計算機視覺、數據挖掘、生物化學和醫療保健在內的最典型應用；第四部分描述了在未來研究中可能重要且有前景的方法和應用的進展。本書內容完整，使其對更廣泛的讀者群體可及，包括（1）高年級本科生和研究生；（2）希望將圖神經網絡應用於其產品和平台的從業者和項目經理；以及（3）希望利用圖神經網絡推進其學科的非計算機科學背景的研究人員。

作者簡介

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.

作者簡介(中文翻譯)

姚馬是密西根州立大學（Michigan State University, MSU）計算機科學與工程系的博士生。他獲得了MSU的傑出研究生獎和FAST獎學金。他在WSDM、ICDM、SDM、WWW、IJCAI、SIGIR和KDD等頂級會議上發表了多篇論文，這些論文被引用數百次。他是AAAI'20、KDD'20和AAAI'21上GNN（圖神經網絡）教程的主要組織者和演講者，受到了廣泛的關注和好評。他曾擔任多個知名會議和期刊的程序委員會成員/審稿人，如AAAI、BigData、IJCAI、TWEB、TKDD和TPAMI。

唐吉良是密西根州立大學計算機科學與工程系的助理教授。此前，他曾是Yahoo Research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職業獎、2019年IJCAI早期職業邀請演講以及7項最佳論文（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。他的研究發表在高排名的期刊和頂級會議上，並獲得了超過12,000次的引用，h-index為55，並受到廣泛的媒體報導。

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