Graph Neural Networks: Foundations, Frontiers, and Applications, Paperback/Lingfei Wu

Momentan nu sunt oferte disponibile.

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. 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 201

Brand

Springer

subcategory

Carte straina/Computing & information technology