Abstract
Graph is a ubiquitous type of data that appears in many real-world applications, including social network analysis, recommendations and financial security. Important as it is, decades of research have developed plentiful computational models to mine graphs. Despite its prosperity, concerns with respect to the potential algorithmic discrimination have been grown recently. Algorithmic fairness on graphs, which aims to mitigate bias introduced or amplified during the graph mining process, is an attractive yet challenging research topic. The first challenge corresponds to the theoretical challenge, where the non-IID nature of graph data may not only invalidate the basic assumption behind many existing studies in fair machine learning, but also introduce new fairness definition(s) based on the inter-correlation between nodes rather than the existing fairness definition(s) in fair machine learning. The second challenge regarding its algorithmic aspect aims to understand how to balance the trade-off between model accuracy and fairness. This tutorial aims to (1) comprehensively review the state-of-the-art techniques to enforce algorithmic fairness on graphs and (2) enlighten the open challenges and future directions. We believe this tutorial could benefit researchers and practitioners from the areas of data mining, artificial intelligence and social science.
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Speaker's Bio
Jian Kang is currently a Ph.D. student in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Prior to that, he was a Ph.D. student in the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University. He received his M.CS. degree in Computer Science from the University of Virginia in 2016 and B.Eng. degree in Telecommunication Engineering from Beijing University of Posts and Telecommunications in 2014. His current research interests lie in large-scale data mining and machine learning, especially on graphs, with a focus on their algorithmic fairness. His research works on related topics have been published at several major conferences and journals in data mining and artificial intelligence. He has also served as a reviewer and a program committee member in top-tier data mining and artificial intelligence venues and journals (e.g., NeurIPS, ICML, ICLR, CIKM, WSDM, JMLR, TKDE, etc). For more information, please refer to his personal website at http://jiank2.web.illinois.edu.
Hanghang Tong is currently an associate professor at Department of Computer Science at University of Illinois at Urbana-Champaign. Before that he was an associate professor at Arizona State University. He received his M.Sc. and Ph.D. degrees from Carnegie Mellon University in 2008 and 2009, both in Machine Learning. His research interest is in large scale data mining for graphs and multimedia. He has received several awards, including AMiner “Most Influential Scholar Award”, honorable mention in data mining (2020), IEEE ICDM Tao Li award (2019), SDM/IBM Early Career Data Mining Research award (2018), NSF CAREER award (2017), ICDM 10-Year Highest Impact Paper award (2015), six best paper awards and several best paper finalists. He has published over 200 refereed articles. He is the Editor-in-Chief of SIGKDD Explorations (ACM) and an associate editor of ACM Computing Surveys (ACM). He is a distinguished member of ACM and a fellow of IEEE. For more information, please refere to his personal website at http://tonghanghang.org.
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