Advanced Deep Graph Learning: Deeper, Faster, Robuster, andUnsupervised

Abstract

Many real data come in the form of non-grid objects, i.e. graphs, from social networks to molecules. Adaptation of deep learning from grid-alike data (e.g. images) to graphs has recently received unprecedented attention from both machine learning and data mining communities, leading to a new cross-domain field---Deep Graph Learning (DGL). Instead of painstaking feature engineering, DGL aims to learn informative representations of graphs in an end-to-end manner. It has exhibited remarkable success in various tasks, such as node/graph classification, link prediction, etc.

Whilst several previous tutorials have been made for the introduction of Graph Neural Networks (GNNs) in TheWebConf, seldom is there focus on the expressivity, trainability, and generalization of DGL algorithms. To make it more prevailing and advanced, this tutorial mainly covers the key achievements of DGL in recent years. Specifically, we will discuss four essential topics, that is, how to design and train deep GNNs in an efficient manner, how to adopt GNNs to cope with large-scale graphs, the adversarial attack on GNNs, and the unsupervised training of GNNs. Meanwhile, we will introduce the applications of DGL towards various domains, including but not limited to drug discovery, computer vision, and social network analysis.

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Tutors Biography

royrong Yu Rong is a Senior researcher of Machine Learning Center in Tencent AI Lab. He received this B.E. degree from Sun Yat-sen University, Guangzhou, China in 2012 and the Ph.D. degree from The Chinese University of Hong Kong in 2016. He joined Tencent AI Lab in June 2017. In Tencent AI Lab, he is working on building the large-scale graph learning framework and applying the deep graph learning model to various applications, such as ADMET prediction and malicious detection. His main research interests include social network analysis, graph neural networks, and large-scale graph systems, with particular focus on the design and efficient training for deep and complex graph learning models. He has published several papers on data mining, machine learning top conferences KDD, WWW, NeurIPS, ICLR, CVPR, ICCV, etc. He has served as a reviewer for KDD, WWW, CVPR, CIKM, WSDM, SDM and other journals such as VLDBJ and TKDE.

 

helendhuang Wenbing Huang is now an assistant researcher at Tsinghua University, awarded as the Shuimu Tsinghua Scholar in 2019 and Tencent Rhino-Bird Visiting Scholar in 2020. He received his Ph.D. degree of computer science and technology from Tsinghua University in 2017 and the bachelor’s degree of mathematics from Beihang University in 2012. His current research mainly lies in the areas of machine learning, computer vision, and robotics, with particular focus on learning on irregular structures including graphs and videos. He has published about 30 peer-reviewed top-tier conference and journal papers, including the Proceedings of NeurIPS, ICLR, ICML, CVPR, etc. He has won the Champion of Autonomous Grasp Challenges in IROS 2016, 2019. He will co-organize the workshop “Human-centric multimedia analysis” in ACMMM 2020. He served as the session chair of “Video: Events, Activities and Surveillance” in IJCAI 2019, the PC member of IJCAI 2019-2020, AAAI 2019-2020, and the reviewer of TPAMI, IJCV, TIP, Neurocomputing, NeurIPS 2019-2020, CVPR 2019-2020, ICML 2019-2020, ICCV 2019, ECCV 2020, AISTATS 2019-2020. He was selected in the list of Top Reviewers in NeurIPS 2019.

 

tingyang_xu Tingyang Xu is a Senior researcher of Machine Learning Center in Tencent AI Lab. He obtained the Ph.D. degree from The University of Connecticut in 2017 and joined Tencent AI Lab in July 2017. In Tencent AI Lab, he is working on deep graph learning, graph generations and applying the deep graph learning model to various applications, such as molecular generation and rumor detection. His main research interests include social network analysis, graph neural networks, and graph generations, with particular focus on design deep and complex graph learning models for molecular generations. He has published several papers on data mining, machine learning top conferences KDD, WWW, NeurIPS, ICLR, CVPR, ICML, etc. He has served as a reviewer for ICML, NeurIPS, KDD, WWW, AAAI, and other journals such as TKDE.

 

yatao_bian Yatao Bian is a Senior researcher of Machine Learning Center in Tencent AI Lab. He received the Ph.D. degree from the Institute for Machine Learning at ETH Zurich and joined Tencent AI Lab in February 2020. He has been an associated Fellow of the Max Planck ETH Center for Learning Systems since June 2015. Before the Ph.D. program he obtained both of his M.Sc.Eng. and B.Sc.Eng. degrees from Shanghai Jiao Tong University. He is now working on graph neural networks, optimization for machine learning and applications such as structure-based drug discovery, 3D protein modelling and social network analysis. He has won the National Champion in AMD China Accelerated Computing Contest 2011-2012. He has published several papers on machine learning top conferences NeurIPS, ICML, ICLR, AISTATS etc. He has served as a reviewer for conferences like ICML, NeurIPS, AISTATS, CVPR, AAAI, STOC and journals such as JMLR.

 

hong_cheng Hong Cheng is an Associate Professor in the Department of Systems Engineering and Engineering Management, Chinese University of Hong Kong. She received the PhD degree from the University of Illinois at Urbana-Champaign in 2008. Her research interests include data mining, database systems, and machine learning. She received research paper awards at ICDE’07, SIGKDD’06, and SIGKDD’05, and the certificate of recognition for the 2009 SIGKDD Doctoral Dissertation Award. She received the 2010 Vice-Chancellor’s Exemplary Teaching Award at the Chinese University of Hong Kong.

 

 

junzhou_huang Junzhou Huang is an Associate Professor in the Computer Science and Engineering department at the University of Texas at Arlington. He also served as the director of machine learning center in Tencent AI Lab. He received the B.E. degree from Huazhong University of Science and Technology, Wuhan, China, the M.S. degree from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, and the Ph.D. degree in Computer Science at Rutgers, The State University of New Jersey. His major research interests include machine learning, computer vision and imaging informatics. He was selected as one of the 10 emerging leaders in multimedia and signal processing by the IBM T.J. Watson Research Center in 2010. His work won the MICCAI Young Scientist Award 2010, the FIMH Best Paper Award 2011, the MICCAI Young Scientist Award Finalist 2011, the STMI Best Paper Award 2012, the NIPS Best Reviewer Award 2013, the MICCAI Best Student Paper Award Finalist 2014 and the MICCAI Best Student Paper Award 2015. He received the NSF CAREER Award in 2016. He enjoys to develop efficient algorithms with nice theoretical guarantees to solve practical problems involved large scale data.

 

junzhou_huang Fuchun Sun, IEEE Fellow, received the Ph.D. degree in computer science and technology from Tsinghua University, Beijing, China, in 1997. He is currently a Professor with the Department of Computer Science and Technology and President of Academic Committee of the Department, Tsinghua University, deputy director of State Key Lab. of Intelligent Technology and Systems, Beijing, China. His research interests include intelligent control and robotics, information sensing and processing in artificial cognitive systems, and networked control systems. He was recognized as a Distinguished Young Scholar in 2006 by the Natural Science Foundation of China. He became a member of the Technical Committee on Intelligent Control of the IEEE Control System Society in 2006. He serves as Editor-in-Chief of International Journal on Cognitive Computation and Systems}, and an Associate Editor for a series of international journals including the IEEE Transactions on Cognitive and Developmental Systems}, the IEEE Transactions on Fuzzy Systems, and the IEEE \Transactions on Systems, Man, and Cybernetics: Systems.