Preprints
Weilin Cong, Jian Kang, Hanghang Tong, Mehrdad Mahdavi. On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method. arXiv:2402.16387.
[Paper]
Zhining Liu, Jian Kang, Hanghang Tong, Yi Chang. IMBENS: Ensemble Class-imbalanced Learning in Python. arXiv:2111.12776.
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
Arjun Subramonian, Jian Kang, Yizhou Sun. Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks. In Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS), 2024.
[Paper]
Yikun Ban, Jiaru Zou, Zihao Li, Yunzhe Qi, Dongqi Fu, Jian Kang, Hanghang Tong, Jingrui He. PageRank Bandits for Link Prediction. In Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS), 2024.
[Paper]
Xinyu He, Jian Kang, Ruizhong Qiu, Fei Wang, Jose Sepulveda, Hanghang Tong. On the Sensitivity of Individual Fairness: Measures and Robust Algorithms. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM), 2024.
[Paper]
Rebecca Salganik, Xiaohao Liu, Yunshan Ma, Jian Kang, Tat-Seng Chua. LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation. In Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2024.
[Paper]
Tianyi Zhao, Jian Kang, Lu Cheng. Conformalized Link Prediction on Graph Neural Networks. In Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2024.
[Paper]
Hyunsik Yoo, Zhichen Zeng, Jian Kang, Zhining Liu, David Zhou, Fei Wang, Eunice Chan, Hanghang Tong. Ensuring User-side Fairness in Dynamic Recommender Systems. In Proceedings of the 2024 ACM Web Conference (WWW), 2024.
[Paper]
Jian Kang, Yinglong Xia, Ross Maciejewski, Jiebo Luo, Hanghang Tong. Deceptive Fairness Attacks on Graphs via Meta Learning. In Proceedings of the 12th International Conference on Learning Representations (ICLR), 2024.
Xiao Lin, Jian Kang, Weilin Cong, Hanghang Tong. BeMap: Balanced Message Passing for Fair Graph Neural Network. In Proceedings of the Second Learning on Graphs Conference (LoG), 2023.
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.