Journal Papers
Tiankai Xie, Yuxin Ma, Jian Kang, Hanghang Tong, Ross Maciejewski. FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models. In IEEE Transactions on Visualization and Computer Graphics, 2021.
[Paper]
Meijia Wang, Jian Kang, Nan Cao, Yinglong Xia, Wei Fan, Hanghang Tong. Graph Ranking Auditing: Problem Definitions and Fast Solutions. In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.
[Paper]
Conference Papers
Weilin Cong, Si Zhang, Jian Kang, Baichuan Yuan, Hao Wu, Xin Zhou, Hanghang Tong, Mehrdad Mahdavi. Do We Really Need Complicated Model Architectures For Temporal Networks?. In Proceedings of the 11th International Conference on Learning Representations (ICLR), 2023. [Oral, 5%]
[Paper] [Code] [Talk (long version)]
Jian Kang, Tiankai Xie, Xintao Wu, Ross Maciejewski, Hanghang Tong. InfoFair: Information-Theoretic Intersectional Fairness. In Proceedings of 2022 IEEE International Conference on Big Data (Big Data), 2022.
Yian Wang, Jian Kang, Yinglong Xia, Jiebo Luo, Hanghang Tong. iFiG: Individually Fair Multi-view Graph Clustering. In Proceedings of 2022 IEEE International Conference on Big Data (Big Data), 2022.
Jian Kang*, Qinghai Zhou*, Hanghang Tong. JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks. In Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2022.
Jian Kang, Yan Zhu, Yinglong Xia, Jiebo Luo, Hanghang Tong. RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network. In Proceedings of the ACM Web Conference (WWW), 2022.
Yushun Dong, Jian Kang, Hanghang Tong, Jundong Li. Individual Fairness for Graph Neural Networks: A Ranking based Approach. In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2021.
Jian Kang, Jingrui He, Ross Maciejewski, Hanghang Tong. InFoRM: Individual Fairness on Graph Mining. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2020.
Jian Kang, Hanghang Tong. N2N: Network Derivative Mining. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), 2019.
Jian Kang*, Scott Freitas*, Haichao Yu, Yinglong Xia, Nan Cao, Hanghang Tong. X-Rank: Explainable Ranking in Complex Multi-layered Networks. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM), 2018.
[Paper]
Jian Kang, Meijia Wang, Nan Cao, Yinglong Xia, Wei Fan, Hanghang Tong. AURORA: Auditing PageRank on Large Graphs. In Proceedings of 2018 IEEE International Conference on Big Data (Big Data), 2018.
[Paper] [Slides] [Code] (check out the updated results in our TKDE paper)
Haichao Yu, Jian Kang, Yinglong Xia, Nan Cao, Hanghang Tong. Visual Mining of Multi-sourced Networks. In Proceedings of the 5th China Visualization and Visual Analytics Conference (ChinaVis), 2018.
Tutorial Proposals
Jian Kang, Hanghang Tong. Algorithmic Fairness on Graphs: State-of-the-Art and Open Challenges. In SIAM International Conference on Data Mining (SDM), 2023.
Jian Kang, Hanghang Tong. Algorithmic Fairness on Graphs: Methods and Trends. In Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2022.
Jian Kang, Hanghang Tong. Fair Graph Mining. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021.