Graph Neural Networks: Foundations, Frontiers, and Applications

Wu, Lingfei, Cui, Peng, Pei, Jian

  • 出版商: Springer
  • 出版日期: 2023-01-05
  • 售價: $3,360
  • 貴賓價: 9.5$3,192
  • 語言: 英文
  • 頁數: 689
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9811660565
  • ISBN-13: 9789811660566
  • 相關分類: 人工智慧Machine LearningDeepLearning
  • 海外代購書籍(需單獨結帳)


Chapter 1. Representation Learning.- Chapter 2. Graph Representation Learning.- Chapter 3. Graph Neural Networks.- Chapter 4. Graph Neural Networks for Node Classification.- Chapter 5. The Expressive Power of Graph Neural Networks.- Chapter 6. Graph Neural Networks: Scalability.- Chapter 7. Interpretability in Graph Neural Networks.- Chapter 8. "Graph Neural Networks: Adversarial Robustness".- Chapter 9. Graph Neural Networks: Graph Classification.- Chapter 10. Graph Neural Networks: Link Prediction.- Chapter 11. Graph Neural Networks: Graph Generation.- Chapter 12. Graph Neural Networks: Graph Transformation.- Chapter 13. Graph Neural Networks: Graph Matching.- Chapter 14. "Graph Neural Networks: Graph Structure Learning". Chapter 15. Dynamic Graph Neural Networks.- Chapter 16. Heterogeneous Graph Neural Networks.- Chapter 17. Graph Neural Network: AutoML.- Chapter 18. Graph Neural Networks: Self-supervised Learning.- Chapter 19. Graph Neural Network in Modern Recommender Systems.- Chapter 20. Graph Neural Network in Computer Vision.- Chapter 21. Graph Neural Networks in Natural Language Processing.- Chapter 22. Graph Neural Networks in Program Analysis.- Chapter 23. Graph Neural Networks in Software Mining.- Chapter 24. "GNN-based Biomedical Knowledge Graph Mining in Drug Development".- Chapter 25. "Graph Neural Networks in Predicting Protein Function and Interactions".- Chapter 26. Graph Neural Networks in Anomaly Detection.- Chapter 27. Graph Neural Networks in Urban Intelligence.


第一章. 表示學習
第二章. 圖形表示學習
第三章. 圖形神經網絡
第四章. 用於節點分類的圖形神經網絡
第五章. 圖形神經網絡的表達能力
第六章. 圖形神經網絡:可擴展性
第七章. 圖形神經網絡中的可解釋性
第八章. 圖形神經網絡:對抗韌性
第九章. 圖形神經網絡:圖形分類
第十章. 圖形神經網絡:鏈接預測
第十一章. 圖形神經網絡:圖形生成
第十二章. 圖形神經網絡:圖形轉換
第十三章. 圖形神經網絡:圖形匹配
第十四章. 圖形神經網絡:圖形結構學習
第十五章. 動態圖形神經網絡
第十六章. 異構圖形神經網絡
第十七章. 圖形神經網絡:自動機器學習
第十八章. 圖形神經網絡:自監督學習
第十九章. 現代推薦系統中的圖形神經網絡
第二十章. 電腦視覺中的圖形神經網絡
第二十一章. 自然語言處理中的圖形神經網絡
第二十二章. 程式分析中的圖形神經網絡
第二十三章. 軟體挖掘中的圖形神經網絡
第二十四章. 基於GNN的生物醫學知識圖形挖掘在藥物開發中
第二十五章. 預測蛋白質功能和相互作用中的圖形神經網絡
第二十六章. 異常檢測中的圖形神經網絡
第二十七章. 城市智能中的圖形神經網絡


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.


Dr. Lingfei Wu是JD.COM硅谷研究中心的首席科学家,领导着一个由30多名机器学习/自然语言处理科学家和软件工程师组成的团队,致力于构建智能电子商务个性化系统。他于2016年获得威廉与玛丽学院计算机科学博士学位。此前,他曾是IBM Thomas J. Watson研究中心的研究员,并领导了一个由10多名研究科学家组成的团队,开发了新颖的图神经网络方法和系统,这导致了IBM研究中的#1人工智能挑战项目以及多个IBM奖项,包括三次杰出技术成就奖。他发表了90多篇顶级会议和期刊论文,并是40多项美国专利的共同发明人。由于他的专利具有很高的商业价值,他获得了八项发明成就奖,并被任命为2020年的IBM主要发明家。他曾获得IEEE ICC'19、AAAI DLGMA'20研讨会和KDD DLG'19等多个会议的最佳论文奖和最佳学生论文奖。他的研究在包括NatureNews、YahooNews、Venturebeat、TechTalks、SyncedReview、Leiphone、QbitAI、MIT News、IBM Research News和SIAM News在内的众多媒体中得到了报道。他共同组织了10多个会议(KDD、AAAI、IEEE BigData),并是Deep Learning on Graphs研讨会的创始联合主席(与AAAI'21、AAAI'20、KDD'21、KDD'20、KDD'19和IEEE BigData'19合作)。他目前担任IEEE Transactions on Neural Networks and Learning Systems、ACM Transactions on Knowledge Discovery from Data和International Journal of Intelligent Systems的副编辑,并定期担任以下主要人工智能/机器学习/自然语言处理会议的SPC/PC成员,包括KDD、IJCAI、AAAI、NIPS、ICML、ICLR和ACL。

Dr. Peng Cui是清华大学计算机科学系的终身副教授。他于2010年从清华大学获得博士学位。他的研究兴趣包括数据挖掘、机器学习和多媒体分析,专注于网络表示学习、因果推断和稳定学习、社交动态建模和用户行为建模等领域。他热衷于推动因果推断和机器学习的融合与整合,解决当今人工智能技术的基本问题,包括可解释性、稳定性和公平性问题。他被认为是ACM杰出科学家、CCF杰出会员和IEEE高级会员。他在机器学习和数据挖掘领域的著名会议和期刊上发表了100多篇论文。他是网络嵌入领域中被引用最多的作者之一。他提出的一些网络嵌入算法在学术界和工业界产生了重大影响。他最近的研究获得了IEEE多媒体最佳部门论文奖、IEEE ICDM 2015最佳学生论文奖、IEEE ICME 2014最佳论文奖、ACM MM12 Grand Challenge Multimodal奖、MMM13最佳论文奖,并分别被选入2014年和2016年KDD特刊的最佳论文。他曾担任CIKM2019和MMM2020的PC联席主席,ICML、KDD、WWW、IJCAI、AAAI等的SPC或领域主席,以及IEEE TKDE(2017-)、IEEE TBD(2019-)、ACM TIST(2018-)和ACM TOMM(2016-)等的副编辑。他于2015年获得ACM中国新星奖,并于2018年获得CCF-IEEE CS青年科学家奖。

Dr. Jian Pei是西蒙菲莎大学计算机科学学院的教授。他是数据科学、大数据、数据挖掘和数据库系统等领域的知名研究者。他的专长是为新颖的数据密集应用开发有效和高效的数据分析技术,并将他的研究成果转化为产品和商业实践。他被认为是加拿大皇家学会的院士。