Graph Neural Networks: Foundations, Frontiers, and Applications
暫譯: 圖神經網絡：基礎、前沿與應用

第1章. 表示學習
第2章. 圖形表示學習
第3章. 圖形神經網絡
第4章. 圖形神經網絡於節點分類
第5章. 圖形神經網絡的表達能力
第6章. 圖形神經網絡：可擴展性
第7章. 圖形神經網絡中的可解釋性
第8章. 圖形神經網絡：對抗穩健性
第9章. 圖形神經網絡：圖形分類
第10章. 圖形神經網絡：連結預測
第11章. 圖形神經網絡：圖形生成
第12章. 圖形神經網絡：圖形轉換
第13章. 圖形神經網絡：圖形匹配
第14章. 圖形神經網絡：圖形結構學習
第15章. 動態圖形神經網絡
第16章. 異質圖形神經網絡
第17章. 圖形神經網絡：自動機器學習
第18章. 圖形神經網絡：自我監督學習
第19章. 現代推薦系統中的圖形神經網絡
第20章. 計算機視覺中的圖形神經網絡
第21章. 自然語言處理中的圖形神經網絡
第22章. 程式分析中的圖形神經網絡
第23章. 軟體挖掘中的圖形神經網絡
第24章. 基於GNN的生物醫學知識圖譜挖掘於藥物開發
第25章. 圖形神經網絡於預測蛋白質功能和相互作用
第26章. 圖形神經網絡於異常檢測
第27章. 圖形神經網絡於城市智能

Dr. Lingfei Wu is a Principal Scientist at JD.COM Silicon Valley Research Center, leading a team of 30+ machine learning/natural language processing scientists and software engineers to build intelligent e-commerce personalization system. He earned his Ph.D. degree in computer science from the College of William and Mary in 2016. Previously, he was a research staff member at IBM Thomas J. Watson Research Center and led a 10+ research scientist team for developing novel Graph Neural Networks methods and systems, which leads to the #1 AI Challenge Project in IBM Research and multiple IBM Awards including three-time Outstanding Technical Achievement Award. He has published more than 90 top-ranked conference and journal papers, and is a co-inventor of more than 40 filed US patents. Because of the high commercial value of his patents, he has received eight invention achievement awards and has been appointed as IBM Master Inventors, class of 2020. He was the recipients of the Best Paper Award and Best Student Paper Award of several conferences such as IEEE ICC'19, AAAI workshop on DLGMA'20 and KDD workshop on DLG'19. His research has been featured in numerous media outlets, including NatureNews, YahooNews, Venturebeat, TechTalks, SyncedReview, Leiphone, QbitAI, MIT News, IBM Research News, and SIAM News. He has co-organized 10+ conferences (KDD, AAAI, IEEE BigData) and is the founding co-chair for Workshops of Deep Learning on Graphs (with AAAI'21, AAAI'20, KDD'21, KDD'20, KDD'19, and IEEE BigData'19). He has currently served as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, ACM Transactions on Knowledge Discovery from Data and International Journal of Intelligent Systems, and regularly served as a SPC/PC member of the following major AI/ML/NLP conferences including KDD, IJCAI, AAAI, NIPS, ICML, ICLR, and ACL.

Dr. Peng Cui is an Associate Professor with tenure at Department of Computer Science in Tsinghua University. He obtained his PhD degree from Tsinghua University in 2010. His research interests include data mining, machine learning and multimedia analysis, with expertise on network representation learning, causal inference and stable learning, social dynamics modeling, and user behavior modeling, etc. He is keen to promote the convergence and integration of causal inference and machine learning, addressing the fundamental issues of today's AI technology, including explainability, stability and fairness issues. He is recognized as a Distinguished Scientist of ACM, Distinguished Member of CCF and Senior Member of IEEE. He has published more than 100 papers in prestigious conferences and journals in machine learning and data mining. He is one of the most cited authors in network embedding. A number of his pro- posed algorithms on network embedding generate substantial impact in academia and industry. His recent research won the IEEE Multimedia Best Department Paper Award, IEEE ICDM 2015 Best Student Paper Award, IEEE ICME 2014 Best Paper Award, ACM MM12 Grand Challenge Multimodal Award, MMM13 Best Paper Award, and were selected into the Best of KDD special issues in 2014 and 2016, respectively. He was PC co-chair of CIKM2019 and MMM2020, SPC or area chair of ICML, KDD, WWW, IJCAI, AAAI, etc., and Associate Editors of IEEE TKDE (2017-), IEEE TBD (2019-), ACM TIST(2018-), and ACM TOMM (2016-) etc. He received ACM China Rising Star Award in 2015, and CCF-IEEE CS Young Scientist Award in 2018.

Dr. Jian Pei is a Professor in the School of Computing Science at Simon Fraser University. He is a well-known leading researcher in the general areas of data science, big data, data mining, and database systems. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications, and transferring his research results to products and business practice. He is recognized as a Fellow of the Royal Society of Canada (Canada's national academy), the Canadian Academy of Engineering, the Association of Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE). He is one of the most cited authors in data mining, database systems, and information retrieval. Since 2000, he has published one textbook, two monographs and over 300 research papers in refereed journals and conferences, which have been cited extensively by others. His research has generated remarkable impact substantially beyond academia. For example, his algorithms have been adopted by industry in production and popular open-source software suites. Jian Pei also demonstrated outstanding professional leadership in many academic organizations and activities. He was the editor-in-chief of the IEEE Transactions of Knowledge and Data Engineering (TKDE) in 2013-16, the chair of the Special Interest Group on Knowledge Discovery in Data (SIGKDD) of the As- sociation for Computing Machinery (ACM) in 2017-2021, and a general co-chair or program committee co-chair of many premier conferences. He maintains a wide spectrum of industry relations with both global and local industry partners. He is an active consultant and coach for industry on enterprise data strategies, healthcare informatics, network security intelligence, computational finance, and smart retail. He received many prestigious awards, including the 2017 ACM SIGKDD Innovation Award, the 2015 ACM SIGKDD Service Award, the 2014 IEEE ICDM Re- search Contributions Award, the British Columbia Innovation Council 2005 Young Innovator Award, an NSERC 2008 Discovery Accelerator Supplements Award (100 awards cross the whole country), an IBM Faculty Award (2006), a KDD Best Ap- plication Paper Award (2008), an ICDE Influential Paper Award (2018), a PAKDD Best Paper Award (2014), a PAKDD Most Influential Paper Award (2009), and an IEEE Outstanding Paper Award (2007).

Dr. Liang Zhao is an assistant professor at the Department of Compute Science at Emory University. Before that, he was an assistant professor in the Department of Information Science and Technology and the Department of Computer Science at George Mason University. He obtained his PhD degree in 2016 from Computer Science Department at Virginia Tech in the United States. His research interests include data mining, artificial intelligence, and machine learning, with special interests in spatiotemporal and network data mining, deep learning on graphs, nonconvex optimization, model parallelism, event prediction, and interpretable machine learning. He received AWS Ma- chine Learning Research Award in 2020 from Amazon Company for his research on distributed graph neural networks. He won NSF Career Award in 2020 awarded by National Science Foundation for his research on deep learning for spatial networks, and Jeffress Trust Award in 2019 for his research on deep generative models for bio- molecules, awarded by Jeffress Memorial Trust Foundation and Bank of America. He won the Best Paper Award in the 19th IEEE International Conference on Data Mining (ICDM 2019) for the paper of his lab on deep graph transformation. He has also won Best Paper Award Shortlist in the 27th Web Conference (WWW 2021) for deep generative models. He was selected as "Top 20 Rising Star in Data Mining" by Microsoft Search in 2016 for his research on spatiotemporal data mining. He has also won Outstanding Doctoral Student in the Department of Computer Science at Virginia Tech in 2017. He is awarded as CI-Fellow Mentor 2021 by the Computing Community Consortium for his research on deep learning for spatial data. He has published numerous research papers in top-tier conferences and journals such as KDD, TKDE, ICDM, ICLR, Proceedings of the IEEE, ACM Computing Surveys, TKDD, IJCAI, AAAI, and WWW. He has been serving as organizers such as publication chair, poster chair, and session chair for many top-tier conferences such as SIGSPATIAL, KDD, ICDM, and CIKM.

吳靈飛博士是京東（JD.COM）矽谷研究中心的首席科學家，領導一支由30多名機器學習/自然語言處理科學家和軟體工程師組成的團隊，致力於構建智能電子商務個性化系統。他於2016年在威廉與瑪麗學院獲得計算機科學博士學位。此前，他曾是IBM托馬斯·J·沃森研究中心的研究成員，領導一支10多人的研究科學家團隊，開發新穎的圖神經網絡方法和系統，這些工作使他們的項目成為IBM研究的第一名AI挑戰項目，並獲得多項IBM獎項，包括三次傑出技術成就獎。他已發表90多篇頂級會議和期刊論文，並是40多項美國專利的共同發明人。由於其專利的高商業價值，他獲得了八項發明成就獎，並於2020年被任命為IBM大師發明家。他曾獲得多個會議的最佳論文獎和最佳學生論文獎，如IEEE ICC'19、AAAI DLGMA'20研討會和KDD DLG'19研討會。他的研究曾在多家媒體上報導，包括NatureNews、YahooNews、Venturebeat、TechTalks、SyncedReview、Leiphone、QbitAI、MIT News、IBM Research News和SIAM News。他共同組織了10多個會議（KDD、AAAI、IEEE BigData），並是深度學習圖形研討會的創始共同主席（包括AAAI'21、AAAI'20、KDD'21、KDD'20、KDD'19和IEEE BigData'19）。目前，他擔任IEEE神經網絡與學習系統期刊、ACM數據知識發現期刊和國際智能系統期刊的副編輯，並定期擔任KDD、IJCAI、AAAI、NIPS、ICML、ICLR和ACL等主要AI/ML/NLP會議的SPC/PC成員。

崔鵬博士是清華大學計算機科學系的終身副教授。他於2010年在清華大學獲得博士學位。他的研究興趣包括數據挖掘、機器學習和多媒體分析，專長於網絡表示學習、因果推斷和穩定學習、社會動態建模和用戶行為建模等。他熱衷於促進因果推斷與機器學習的融合與整合，解決當今AI技術的基本問題，包括可解釋性、穩定性和公平性問題。他被認定為ACM的傑出科學家、CCF的傑出成員和IEEE的高級成員。他在機器學習和數據挖掘的知名會議和期刊上發表了100多篇論文。他是網絡嵌入領域最被引用的作者之一。他提出的多個網絡嵌入算法在學術界和產業界產生了重大影響。他最近的研究獲得了IEEE多媒體最佳部門論文獎、IEEE ICDM 2015最佳學生論文獎、IEEE ICME 2014最佳論文獎、ACM MM12大挑戰多模態獎、MMM13最佳論文獎，並於2014年和2016年分別入選KDD特刊的最佳論文。他曾擔任CIKM2019和MMM2020的程序委員會共同主席，並擔任ICML、KDD、WWW、IJCAI、AAAI等會議的SPC或區域主席，以及IEEE TKDE（2017-）、IEEE TBD（2019-）、ACM TIST（2018-）和ACM TOMM（2016-）等期刊的副編輯。他於2015年獲得ACM中國新星獎，並於2018年獲得CCF-IEEE CS青年科學家獎。

裴健博士是西門菲莎大學計算科學學院的教授。他是數據科學、大數據、數據挖掘和數據庫系統領域的知名研究者。他的專長在於為新型數據密集型應用開發有效且高效的數據分析技術，並將其研究成果轉化為產品和商業實踐。他被認定為加拿大皇家學會（加拿大國家學院）、加拿大工程學院、計算機協會（ACM）和電氣與電子工程師學會（IEEE）的院士。他是數據挖掘、數據庫系統和信息檢索領域最被引用的作者之一。自2000年以來，他發表了一本教科書、兩本專著和300多篇經過同行評審的期刊和會議論文，這些論文被廣泛引用。他的研究在學術界以外產生了顯著影響。例如，他的算法已被業界在生產和流行的開源軟體套件中採用。裴健在許多學術組織和活動中展現了卓越的專業領導力。他曾擔任IEEE知識與數據工程期刊（TKDE）的主編（2013-2016）、計算機協會（ACM）數據知識發現特別興趣小組（SIGKDD）主席（2017-2021），以及多個頂級會議的總共同主席或程序委員會共同主席。他與全球和本地行業夥伴保持著廣泛的行業關係。他是企業數據策略、醫療信息學、網絡安全情報、計算金融和智能零售等領域的活躍顧問和教練。他獲得了多項著名獎項，包括2017年ACM SIGKDD創新獎、2015年ACM SIGKDD服務獎、2014年IEEE ICDM研究貢獻獎、英哥倫比亞創新委員會2005年青年創新者獎、NSERC 2008年發現加速補助獎（全國100項獎項）、IBM教職員獎（2006年）、KDD最佳應用論文獎（2008年）、ICDE影響力論文獎（2018年）、PAKDD最佳論文獎（2014年）、PAKDD最具影響力論文獎（2009年）和IEEE傑出論文獎（2007年）。

趙亮博士是埃默里大學計算機科學系的助理教授。在此之前，他曾是喬治梅森大學信息科學與技術系和計算機科學系的助理教授。他於2016年在美國維吉尼亞理工大學計算機科學系獲得博士學位。他的研究興趣包括數據挖掘、人工智慧和機器學習，特別關注時空和網絡數據挖掘、圖上的深度學習、非凸優化、模型並行性、事件預測和可解釋的機器學習。他於2020年因其在分佈式圖神經網絡方面的研究獲得了亞馬遜公司的AWS機器學習研究獎。他於2020年因其在空間網絡深度學習方面的研究獲得了國家科學基金會的NSF職業獎，並於2019年因其在生物分子深度生成模型方面的研究獲得了Jeffress Memorial Trust Foundation和美國銀行頒發的Jeffress Trust獎。他的實驗室在第19屆IEEE國際數據挖掘會議（ICDM 2019）上獲得最佳論文獎。他的深度生成模型在第27屆網絡會議（WWW 2021）上獲得最佳論文獎入圍。他於2016年因其在時空數據挖掘方面的研究被微軟搜索選為“數據挖掘領域的20位新星”之一。他於2017年獲得維吉尼亞理工大學計算機科學系的傑出博士生獎。他因其在空間數據深度學習方面的研究被計算社區協會授予2021年CI-Fellow導師。他在KDD、TKDE、ICDM、ICLR、IEEE會議論文集、ACM計算調查、TKDD、IJCAI、AAAI和WWW等頂級會議和期刊上發表了大量研究論文。他在SIGSPATIAL、KDD、ICDM和CIKM等多個頂級會議中擔任出版主席、海報主席和會議主席等組織者。