报告主题：可信图学习

报 告 人：夏锋

报告时间：2022年11月4日（周五）上午9：30-10：30

报告网址：腾讯会议：780-633-074

报告摘要：

  Graphs (or networks) are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs or graph machine learning) is gaining huge attention from both researchers and practitioners. Graph learning proves effective for many tasks in real-world applications, such as regression, classification, clustering, matching, and ranking. Over the past few years, a lot of graph learning models and algorithms (e.g., graph neural networks, network embedding, network representation learning, etc.) have been developed. Nevertheless, the field of graph learning is facing many challenges deriving from, e.g., fundamental theory and models, algorithms and methods, supporting tools and platforms, and real-world deployment and engineering. This talk will give an overview of the state of the art of trustworthy graph learning, paying special attention to relevant trends and challenges. Some recent advancements in this field will be showcased.

报告人简介：

  Dr. Feng Xia is currently an Associate Professor in Institute of Innovation, Science and Sustainability, Federation University Australia. He was a Full Professor and Associate Dean of Research in School of Software, Dalian University of Technology (DUT), China. He is/was on the Editorial Boards of over 10 int’l journals. He has served as the General Chair, Program Committee Chair, Workshop Chair, or Publicity Chair of over 30 int’l conferences and workshops, and Program Committee Member of over 90 conferences. Dr. Xia has authored/co-authored two books, over 300 scientific papers in int’l journals and conferences (such as IEEE TAI, TKDE, TNNLS, TC, TMC, TPDS, TBD, TCSS, TNSE, TETCI, TETC, THMS, TVT, TITS, TASE, ACM TKDD, TIST, TWEB, TOMM, WWW, AAAI, SIGIR, CIKM, JCDL, EMNLP, and INFOCOM) and 3 book chapters. He was recognized as a Highly Cited Researcher (2019) by Clarivate Analytics (Web of Science). Dr. Xia received a number of prestigious awards, including IEEE DSS 2021 Best Paper Award, IEEE Vehicular Technology Society 2020 Best Land Transportation Paper Award, ACM/IEEE JCDL 2020 The Vannevar Bush Best Paper Honorable Mention, IEEE CSDE 2020 Best Paper Award, WWW 2017 Best Demo Award, IEEE DataCom 2017 Best Paper Award, IEEE UIC 2013 Best Paper Award, and IEEE Access Outstanding Associate Editor. His research interests include data science, artificial intelligence, graph learning, and systems engineering. He is a Senior Member of IEEE and ACM, and ACM Distinguished Speaker. URL: http://xia.ai.