Publications

(* indicates equal contribution)

Preprints

• CLIMB: Class-imbalanced Learning Benchmark on Tabular Data

  Zhining Liu, Zihao Li, Ze Yang, Tianxin Wei, Jian Kang, Yada Zhu, Hendrik Hamann, Jingrui He, Hanghang Tong

  arXiv:2505.17451

  [Webpage] [Benchmark]

• Discovering Knowledge Deficiencies of Language Models on Massive Knowledge Base

  Linxin Song, Xuwei Ding, Jieyu Zhang, Taiwei Shi, Ryotaro Shimizu, Rahul Gupta, Yang Liu, Jian Kang, Jieyu Zhao

  arXiv:2503.23361

  [Webpage] [Code]

• Understanding and Rectifying Safety Perception Distortion in VLMs

  Xiaohan Zou, Jian Kang, George Kesidis, Lu Lin

  arXiv:2502.13095

• On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method

  Weilin Cong, Jian Kang, Hanghang Tong, Mehrdad Mahdavi

  arXiv:2402.16387

• IMBENS: Ensemble Class-imbalanced Learning in Python

  Zhining Liu, Jian Kang, Hanghang Tong, Yi Chang

  arXiv:2111.12776

  [Webpage] [Package]

Journal Papers

• Fair Online Influence Maximization

  Xiangqi Wang, Shaokun Zhang, Jose Efraim Aguilar Escamilla, Qingyun Wu, Xiangliang Zhang, Jian Kang, Huazheng Wang

  TMLR, 2025

• FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models

  Tiankai Xie, Yuxin Ma, Jian Kang, Hanghang Tong, Ross Maciejewski

  TVCG, 2021

• Graph Ranking Auditing: Problem Definitions and Fast Solutions

  Meijia Wang, Jian Kang, Nan Cao, Yinglong Xia, Wei Fan, Hanghang Tong

  TKDE, 2020

Conference Papers

• EVINET: Towards Open-World Graph Learning via Evidential Reasoning Network

  Weijia Guan, Haohui Wang, Jian Kang, Lihui Liu, Dawei Zhou

  KDD 2025

  [Code]

• Non-exchangeable Conformal Prediction for Temporal Graph Neural Networks

  Tuo Wang, Jian Kang, Yujun Yan, Adithya Kulkarni, Dawei Zhou

  KDD 2025

  [Code]

• Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese

  Hanjia Lyu, Jiebo Luo, Jian Kang, Allison Koenecke

  FAccT 2025, IC2S2 2025 (parallel talk)

  [Benchmark]

• Bridging Fairness and Uncertainty: Theoretical Insights and Practical Strategies for Equalized Coverage in GNNs

  Longfeng Wu, Yao Zhou, Jian Kang, Dawei Zhou

  WWW 2025

  [Code]

• Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks

  Arjun Subramonian, Jian Kang, Yizhou Sun

  NeurIPS 2024

  [Code]

• PageRank Bandits for Link Prediction

  Yikun Ban, Jiaru Zou, Zihao Li, Yunzhe Qi, Dongqi Fu, Jian Kang, Hanghang Tong, Jingrui He

  NeurIPS 2024

  [Code]

• On the Sensitivity of Individual Fairness: Measures and Robust Algorithms

  Xinyu He, Jian Kang, Ruizhong Qiu, Fei Wang, Jose Sepulveda, Hanghang Tong

  CIKM 2024

• LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation

  Rebecca Salganik, Xiaohao Liu, Yunshan Ma, Jian Kang, Tat-Seng Chua

  KDD 2024

  [Code]

• Conformalized Link Prediction on Graph Neural Networks

  Tianyi Zhao, Jian Kang, Lu Cheng

  KDD 2024

• Ensuring User-side Fairness in Dynamic Recommender Systems

  Hyunsik Yoo, Zhichen Zeng, Jian Kang, Zhining Liu, David Zhou, Fei Wang, Eunice Chan, Hanghang Tong

  WWW 2024

  [Code]

• Deceptive Fairness Attacks on Graphs via Meta Learning

  Jian Kang, Yinglong Xia, Ross Maciejewski, Jiebo Luo, Hanghang Tong

  ICLR 2024

  [Code]

• BeMap: Balanced Message Passing for Fair Graph Neural Network

  Xiao Lin, Jian Kang, Weilin Cong, Hanghang Tong

  LoG 2023

  [Code]

• Do We Really Need Complicated Model Architectures For Temporal Networks?

  Weilin Cong, Si Zhang, Jian Kang, Baichuan Yuan, Hao Wu, Xin Zhou, Hanghang Tong, Mehrdad Mahdavi

  ICLR 2023 (Oral, 5%)

  [Code] [Code (TGB version)] [Talk (long version)]

• InfoFair: Information-Theoretic Intersectional Fairness

  Jian Kang, Tiankai Xie, Xintao Wu, Ross Maciejewski, Hanghang Tong

  IEEE Big Data 2022

  [Slides] [Code]

• iFiG: Individually Fair Multi-view Graph Clustering

  Yian Wang, Jian Kang, Yinglong Xia, Jiebo Luo, Hanghang Tong

  IEEE Big Data 2022

  [Code]

• JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks

  Jian Kang*, Qinghai Zhou*, Hanghang Tong

  KDD 2022

  [Slides] [Code]

• RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network

  Jian Kang, Yan Zhu, Yinglong Xia, Jiebo Luo, Hanghang Tong

  WWW 2022

  [Slides] [Code]

• Individual Fairness for Graph Neural Networks: A Ranking based Approach

  Yushun Dong, Jian Kang, Hanghang Tong, Jundong Li

  KDD 2021

  [Code]

• InFoRM: Individual Fairness on Graph Mining

  Jian Kang, Jingrui He, Ross Maciejewski, Hanghang Tong

  KDD 2020

  [Slides] [Code]

• N2N: Network Derivative Mining

  Jian Kang, Hanghang Tong

  CIKM 2019

  [Slides] [Code]

• X-Rank: Explainable Ranking in Complex Multi-layered Networks

  Jian Kang*, Scott Freitas*, Haichao Yu, Yinglong Xia, Nan Cao, Hanghang Tong

  CIKM 2018

• AURORA: Auditing PageRank on Large Graphs

  Jian Kang, Meijia Wang, Nan Cao, Yinglong Xia, Wei Fan, Hanghang Tong

  IEEE Big Data 2018

  [Slides] [Code] (check out the updated results in our TKDE paper)

• Visual Mining of Multi-sourced Networks

  Haichao Yu, Jian Kang, Yinglong Xia, Nan Cao, Hanghang Tong

  ChinaVis 2018