AURORA: Auditing PageRank on Large Graphs. - Jian Kang 008; Meijia Wang; Nan Cao 001; Yinglong Xia; Wei Fan 001; Hanghang Tong (IEEE BigData, 2018)

X-Rank: Explainable Ranking in Complex Multi-Layered Networks. - Jian Kang 008; Scott Freitas; Haichao Yu; Yinglong Xia; Nan Cao 001; Hanghang Tong (CIKM, 2018)

AURORA: Auditing PageRank on Large Graphs. - Jian Kang 008; Hanghang Tong; Yinglong Xia; Wei Fan 001 (CoRR, 2018)

N2N: Network Derivative Mining. - Jian Kang 008; Hanghang Tong (CIKM, 2019)

InFoRM: Individual Fairness on Graph Mining. - Jian Kang 008; Jingrui He; Ross Maciejewski; Hanghang Tong (KDD, 2020)

Fair Graph Mining. - Jian Kang 008; Hanghang Tong (CIKM, 2021)

Individual Fairness for Graph Neural Networks: A Ranking based Approach. - Yushun Dong; Jian Kang 008; Hanghang Tong; Jundong Li (KDD, 2021)

MultiFair: Multi-Group Fairness in Machine Learning. - Jian Kang 008; Tiankai Xie; Xintao Wu; Ross Maciejewski; Hanghang Tong (CoRR, 2021)

Graph Ranking Auditing: Problem Definition and Fast Solutions. - Meijia Wang; Jian Kang 008; Nan Cao 001; Yinglong Xia; Wei Fan 001; Hanghang Tong (IEEE Trans. Knowl. Data Eng., 2021)

InfoFair: Information-Theoretic Intersectional Fairness. - Jian Kang 008; Tiankai Xie; Xintao Wu; Ross Maciejewski; Hanghang Tong (IEEE Big Data, 2022)

iFiG: Individually Fair Multi-view Graph Clustering. - Yian Wang 002; Jian Kang 008; Yinglong Xia; Jiebo Luo 001; Hanghang Tong (IEEE Big Data, 2022)

TrustLOG: The First Workshop on Trustworthy Learning on Graphs. - Jian Kang 008; Shuaicheng Zhang; Bo Li 026; Jingrui He; Jian Pei 001; Dawei Zhou 003 (CIKM, 2022)

Algorithmic Fairness on Graphs: Methods and Trends. - Jian Kang 008; Hanghang Tong (KDD, 2022)

JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks. - Jian Kang 008; Qinghai Zhou; Hanghang Tong (KDD, 2022)

RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network. - Jian Kang 008; Yan Zhu; Yinglong Xia; Jiebo Luo 001; Hanghang Tong (WWW, 2022)

FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models. - Tiankai Xie; Yuxin Ma; Jian Kang 008; Hanghang Tong; Ross Maciejewski (IEEE Trans. Vis. Comput. Graph., 2022)

Do We Really Need Complicated Model Architectures For Temporal Networks? - Weilin Cong; Si Zhang; Jian Kang 008; Baichuan Yuan; Hao Wu; Xin Zhou; Hanghang Tong; Mehrdad Mahdavi (ICLR, 2023)

BeMap: Balanced Message Passing for Fair Graph Neural Network. - Xiao Lin; Jian Kang 008; Weilin Cong; Hanghang Tong (LoG, 2023)

Ensuring User-side Fairness in Dynamic Recommender Systems. - Hyunsik Yoo; Zhichen Zeng 001; Jian Kang 008; Zhining Liu 002; David Zhou; Fei Wang 065; Eunice Chan; Hanghang Tong (CoRR, 2023)

Data Quality-aware Graph Machine Learning. - Yu Wang 160; Kaize Ding; Xiaorui Liu; Jian Kang 008; Ryan A. Rossi; Tyler Derr (CIKM, 2024)

On the Sensitivity of Individual Fairness: Measures and Robust Algorithms. - Xinyu He 003; Jian Kang 008; Ruizhong Qiu; Fei Wang 065; Jose Sepulveda; Hanghang Tong (CIKM, 2024)

Deceptive Fairness Attacks on Graphs via Meta Learning. - Jian Kang 008; Yinglong Xia; Ross Maciejewski; Jiebo Luo 001; Hanghang Tong (ICLR, 2024)

LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation. - Rebecca Salganik; Xiaohao Liu; Yunshan Ma; Jian Kang 008; Tat-Seng Chua (KDD, 2024)

Conformalized Link Prediction on Graph Neural Networks. - Tianyi Zhao; Jian Kang 008; Lu Cheng 001 (KDD, 2024)

PageRank Bandits for Link Prediction. - Yikun Ban; Jiaru Zou; Zihao Li 006; Yunzhe Qi; Dongqi Fu; Jian Kang 008; Hanghang Tong; Jingrui He (NeurIPS, 2024)

Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks. - Arjun Subramonian; Jian Kang 008; Yizhou Sun (NeurIPS, 2024)

TrustLOG: The Second Workshop on Trustworthy Learning on Graphs. - Jingrui He; Jian Kang 008; Fatemeh Nargesian; Haohui Wang; An Zhang 003; Dawei Zhou 003 (WWW (Companion Volume), 2024)

Ensuring User-side Fairness in Dynamic Recommender Systems. - Hyunsik Yoo; Zhichen Zeng 001; Jian Kang 008; Ruizhong Qiu; David Zhou; Zhining Liu 002; Fei Wang 065; Charlie Xu; Eunice Chan; Hanghang Tong (WWW, 2024)

On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method. - Weilin Cong; Jian Kang 008; Hanghang Tong; Mehrdad Mahdavi (CoRR, 2024)

Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese. - Hanjia Lyu; Jiebo Luo 001; Jian Kang 008; Allison Koenecke (FAccT, 2025)

Bridging Fairness and Uncertainty: Theoretical Insights and Practical Strategies for Equalized Coverage in GNNs. - Longfeng Wu; Yao Zhou; Jian Kang 008; Dawei Zhou 003 (WWW, 2025)

CLIMB: Class-imbalanced Learning Benchmark on Tabular Data. - Zhining Liu 002; Zihao Li 006; Ze Yang; Tianxin Wei; Jian Kang 008; Yada Zhu; Hendrik F. Hamann; Jingrui He; Hanghang Tong (CoRR, 2025)

EVINET: Towards Open-World Graph Learning via Evidential Reasoning Network. - Weijie Guan; Haohui Wang; Jian Kang 008; Lihui Liu; Dawei Zhou 003 (CoRR, 2025)

Non-exchangeable Conformal Prediction for Temporal Graph Neural Networks. - Tuo Wang; Jian Kang 008; Yujun Yan; Adithya Kulkarni; Dawei Zhou 003 (CoRR, 2025)