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.
Slides
Schedule
- Time: 9:30am - 12:30am EDT, August 17, 2022
- Location: Room 201, Walter E. Washington Convention Center
Outline
- Introduction
- Background and motivations
- Problem definitions and settings
- Key challenges
- Part I: Group Fairness on Graphs
- Fair graph ranking
- Fair graph clustering
- Fair graph embedding
- Part II: Individual Fairness on Graphs
- Optimization-based method
- Ranking-based method
- Part III: Other Fairness on Graphs
- Counterfactual fairness
- Degree-related fairness
- Part IV: Beyond Fairness on Graphs
- Related problems
- Explainability
- Accountability
- Robustness
- Part V: Future Trends
- Fairness on dynamic graphs
- Benchmark and evaluation metrics
- Fairness vs. other social aspects
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.
References
- Tsioutsiouliklis, S., Pitoura, E., Tsaparas, P., Kleftakis, I., & Mamoulis, N. (2021). Fairness-Aware PageRank. WWW 2021.
- Tsioutsiouliklis, S., Pitoura, E., Semertzidis, K., & Tsaparas, P. (2022). Link Recommendations for PageRank Fairness. WWW 2022.
- Kleindessner, M., Samadi, S., Awasthi, P., & Morgenstern, J. (2019). Guarantees for Spectral Clustering with Fairness Constraints. ICML 2019.
- Bose, A., & Hamilton, W.. Compositional Fairness Constraints for Graph Embeddings. ICML 2019.
- Rahman, T., Surma, B., Backes, M., & Zhang, Y. (2019). Fairwalk: Towards Fair Graph Embedding. IJCAI 2019.
- Khajehnejad, A., Khajehnejad, M., Babaei, M., Gummadi, K. P., Weller, A., & Mirzasoleiman, B. (2022). CrossWalk: Fairness-enhanced Node Representation Learning. AAAI 2022.
- Dai, E., & Wang, S. (2021). Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information. WSDM 2021.
- Kang, J., He, J., Maciejewski, R., & Tong, H. (2020). InFoRM: Individual Fairness on Graph Mining. KDD 2020.
- Dong, Y., Kang, J., Tong, H., & Li, J. (2021). Individual Fairness for Graph Neural Networks: A Ranking based Approach. KDD 2021.
- Agarwal, C., Lakkaraju, H., & Zitnik, M. (2021). Towards a Unified Framework for Fair and Stable Graph Representation Learning. UAI 2021.
- Ma, J., Guo, R., Wan, M., Yang, L., Zhang, A., & Li, J. (2022). Learning Fair Node Representations with Graph Counterfactual Fairness. WSDM 2022.
- Tang, X., Yao, H., Sun, Y., Wang, Y., Tang, J., Aggarwal, C., ... & Wang, S. (2020). Investigating and Mitigating Degree-related Biases in Graph Convolutional Networks. CIKM 2020.
- Kang, J., Zhu, Y., Xia, Y., Luo, J., & Tong, H. (2022). RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network. WWW 2022.
- Ying, R., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019). GNNExplainer: Generating Explanations for Graph Neural Networks. NeurIPS 2019.
- Kang, J., Wang, M., Cao, N., Xia, Y., Fan, W., & Tong, H. (2018). AURORA: Auditing PageRank on Large Graphs. Big Data 2018.
- Kang, J., & Tong, H. (2019). N2N: Network Derivative Mining. CIKM 2019.
- Zügner, D., Akbarnejad, A., & Günnemann, S. (2018). Adversarial Attacks on Neural Networks for Graph Data. KDD 2018.
- Entezari, N., Al-Sayouri, S. A., Darvishzadeh, A., & Papalexakis, E. E. (2020). All You Need is Low (Rank): Defending Against Adversarial Attacks on Graphs. WSDM 2020.