Fair Graph Mining

Tutorial Website http://jiank2.web.illinois.edu/tutorial/cikm21/fair_graph_mining.html

Tutorial Schedule

  • We plan to hold 2 sessions for our tutorial. The first session will be live presentation. And we plan to play the pre-recorded videos in the second session. Detailed time (in UTC time) of each session are as follows.
  • 1st Session (live presentation): Nov.4th, 5pm - 8pm UTC
  • 2nd Session (recordings): Nov. 5th 2am - 5am UTC

Tutorial Description

In today’s increasingly connected world, graph mining plays a pivotal role in many real-world application domains, including social network analysis, recommendations, marketing and financial security. Tremendous efforts have been made to develop a wide range of computational models. However, recent studies have revealed that many widely-applied graph mining models could suffer from potential discrimination. Fairness on graph mining aims to develop strategies in order to mitigate bias introduced/amplified during the mining process. The unique challenges of enforcing fairness on graph mining include (1) theoretical challenge on non-IID nature of graph data, which may invalidate the basic assumption behind many existing studies in fair machine learning, and (2) algorithmic challenge on the dilemma of balancing model accuracy and fairness. This tutorial aims to (1) present a comprehensive review of state-of-the-art techniques in fairness on graph mining and (2) identify the open challenges and future trends. In particular, we start with reviewing the background, problem definitions, unique challenges and related problems; then we will focus on an in-depth overview of (1) recent techniques in enforcing group fairness, individual fairness and other fairness notions in the context of graph mining, and (2) future directions in studying algorithmic fairness on graphs. We believe this tutorial could be attractive to researchers and practitioners in areas including data mining, artificial intelligence, social science and beneficial to a plethora of real-world application domains.

Tutorial Organisers

  • Head shot of Jian Kang
    Jian Kang
    University of Illinois at Urbana-Champaign
    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 machine learning. 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.
  • Head shot of Hanghang Tong
    Hanghang Tong
    University of Illinois at Urbana-Champaign
    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 School of Computing, Informatics, and Decision Systems Engineering (CIDSE), 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 SDM/IBM Early Career Data Mining Research award (2018), NSF CAREER award (2017), ICDM 10-Year Highest Impact Paper award (2015), four best paper awards (TUP'14, CIKM'12, SDM'08, ICDM'06), seven 'bests of conference', 1 best demo, honorable mention (SIGMOD'17), and 1 best demo candidate, second place (CIKM'17). He has published over 200 refereed articles. He is the Editor-in-Chief of SIGKDD Explorations (ACM), %an action editor of Data Mining and Knowledge Discovery (Springer), and an associate editor of Knowledge and Information Systems (Springer) and Computing Surveys (ACM); and has served as a program committee member in multiple data mining, database and artificial intelligence venues (e.g., SIGKDD, CIKM, SIGMOD, AAAI, WWW, etc.). He has given several tutorials at top-tier conferences, such as IEEE Big Data 2015, SDM 2016, WSDM 2018, KDD 2018, CIKM 2020, etc. For more information, please refere to his personal website at http://tonghanghang.org.

Tutorial Abstract

In today's increasingly connected world, graph mining plays a pivotal role in many real-world application domains, including social network analysis, recommendations, marketing and financial security. Tremendous efforts have been made to develop a wide range of computational models. However, recent studies have revealed that many widely-applied graph mining models could suffer from potential discrimination. Fairness on graph mining aims to develop strategies in order to mitigate bias introduced/amplified during the mining process. The unique challenges of enforcing fairness on graph mining include (1) theoretical challenge on non-IID nature of graph data, which may invalidate the basic assumption behind many existing studies in fair machine learning, and (2) algorithmic challenge on the dilemma of balancing model accuracy and fairness. This tutorial aims to (1) present a comprehensive review of state-of-the-art techniques in fairness on graph mining and (2) identify the open challenges and future trends. In particular, we start with reviewing the background, problem definitions, unique challenges and related problems; then we will focus on an in-depth overview of (1) recent techniques in enforcing group fairness, individual fairness and other fairness notions in the context of graph mining, and (2) future directions in studying algorithmic fairness on graphs. We believe this tutorial could be attractive to researchers and practitioners in areas including data mining, artificial intelligence, social science and beneficial to a plethora of real-world application domains.