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ORCIDJian Kang 0008
Person information
- affiliation: University of Rochester, Rochester, NY, USA
- affiliation (PhD): University of Illinois Urbana-Champaign, IL, USA
Other persons with the same name
- Jian Kang — disambiguation page
- Jian Kang 0001
![[0000-0002-5572-7405]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0002-5572-7405")
— Jilin University, Department of Computer Science & Technology, Changchun, China - Jian Kang 0002 — University of Sheffield, School of Architecture, UK (and 3 more)
- Jian Kang 0003 — University of Michigan, Department of Biostatistics, Ann Arbor, MI, USA
- Jian Kang 0004 — Chinese Academy of Sciences, Key Laboratory of Remote Sensing of Gansu Province, Lanzhou, China
- Jian Kang 0005 — Technical University of Munich, Chair of Signal Processing in Earth Observation, Germany (and 1 more)
- Jian Kang 0006 — Tsinghua University, Department of Electronic Engineering, TNList, Beijing, China
- Jian Kang 0007 — Oregon State University, School of Electrical Engineering and Computer Science, Corvallis, OR, USA
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2020 – today
- 2024
[c23]Xinyu He, Jian Kang, Ruizhong Qiu, Fei Wang, Jose Sepulveda, Hanghang Tong:
On the Sensitivity of Individual Fairness: Measures and Robust Algorithms. CIKM 2024: 829-838- [c22]Yu Wang, Kaize Ding, Xiaorui Liu, Jian Kang, Ryan A. Rossi, Tyler Derr:
Data Quality-aware Graph Machine Learning. CIKM 2024: 5534-5537 - [c21]Jian Kang, Yinglong Xia, Ross Maciejewski, Jiebo Luo, Hanghang Tong:
Deceptive Fairness Attacks on Graphs via Meta Learning. ICLR 2024 - [c20]Rebecca Salganik, Xiaohao Liu, Yunshan Ma, Jian Kang, Tat-Seng Chua:
LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation. KDD 2024: 2524-2535 - [c19]Tianyi Zhao, Jian Kang, Lu Cheng:
Conformalized Link Prediction on Graph Neural Networks. KDD 2024: 4490-4499 - [c18]Yikun Ban, Jiaru Zou, Zihao Li, Yunzhe Qi, Dongqi Fu, Jian Kang, Hanghang Tong, Jingrui He:
PageRank Bandits for Link Prediction. NeurIPS 2024 - [c17]Arjun Subramonian, Jian Kang, Yizhou Sun:
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks. NeurIPS 2024 - [c16]Jingrui He, Jian Kang, Fatemeh Nargesian, Haohui Wang, An Zhang, Dawei Zhou:
TrustLOG: The Second Workshop on Trustworthy Learning on Graphs. WWW (Companion Volume) 2024: 1785-1788 - [c15]Hyunsik Yoo, Zhichen Zeng, Jian Kang, Ruizhong Qiu, David Zhou, Zhining Liu, Fei Wang, Charlie Xu, Eunice Chan, Hanghang Tong:
Ensuring User-side Fairness in Dynamic Recommender Systems. WWW 2024: 3667-3678 - [i13]Weilin Cong, Jian Kang, Hanghang Tong, Mehrdad Mahdavi:
On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method. CoRR abs/2402.16387 (2024) - [i12]Arjun Subramonian, Jian Kang, Yizhou Sun:
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks. CoRR abs/2404.03139 (2024) - [i11]Rebecca Salganik, Xiaohao Liu, Yunshan Ma, Jian Kang, Tat-Seng Chua:
LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation. CoRR abs/2406.14333 (2024) - [i10]Tianyi Zhao, Jian Kang, Lu Cheng:
Conformalized Link Prediction on Graph Neural Networks. CoRR abs/2406.18763 (2024) - [i9]Yikun Ban, Jiaru Zou, Zihao Li, Yunzhe Qi, Dongqi Fu, Jian Kang, Hanghang Tong, Jingrui He:
PageRank Bandits for Link Prediction. CoRR abs/2411.01410 (2024) - 2023
- [c14]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? ICLR 2023 - [c13]Xiao Lin, Jian Kang, Weilin Cong, Hanghang Tong:
BeMap: Balanced Message Passing for Fair Graph Neural Network. LoG 2023: 37 - [i8]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? CoRR abs/2302.11636 (2023) - [i7]Xiao Lin, Jian Kang, Weilin Cong, Hanghang Tong:
BeMap: Balanced Message Passing for Fair Graph Neural Network. CoRR abs/2306.04107 (2023) - [i6]Hyunsik Yoo, Zhichen Zeng, Jian Kang, Zhining Liu, David Zhou, Fei Wang, Eunice Chan, Hanghang Tong:
Ensuring User-side Fairness in Dynamic Recommender Systems. CoRR abs/2308.15651 (2023) - [i5]Jian Kang, Yinglong Xia, Ross Maciejewski, Jiebo Luo, Hanghang Tong:
Deceptive Fairness Attacks on Graphs via Meta Learning. CoRR abs/2310.15653 (2023) - 2022
- [j2]Tiankai Xie, Yuxin Ma, Jian Kang, Hanghang Tong, Ross Maciejewski:
FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models. IEEE Trans. Vis. Comput. Graph. 28(1): 368-377 (2022) - [c12]Yian Wang, Jian Kang, Yinglong Xia, Jiebo Luo, Hanghang Tong:
iFiG: Individually Fair Multi-view Graph Clustering. IEEE Big Data 2022: 329-338 - [c11]Jian Kang, Tiankai Xie, Xintao Wu, Ross Maciejewski, Hanghang Tong:
InfoFair: Information-Theoretic Intersectional Fairness. IEEE Big Data 2022: 1455-1464 - [c10]Jian Kang, Shuaicheng Zhang, Bo Li, Jingrui He, Jian Pei, Dawei Zhou:
TrustLOG: The First Workshop on Trustworthy Learning on Graphs. CIKM 2022: 5169-5170 - [c9]Jian Kang, Qinghai Zhou, Hanghang Tong:
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks. KDD 2022: 742-752 - [c8]Jian Kang, Hanghang Tong:
Algorithmic Fairness on Graphs: Methods and Trends. KDD 2022: 4798-4799 - [c7]Jian Kang, Yan Zhu, Yinglong Xia, Jiebo Luo, Hanghang Tong:
RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network. WWW 2022: 1214-1225 - [i4]Jian Kang, Yan Zhu, Yinglong Xia, Jiebo Luo, Hanghang Tong:
RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network. CoRR abs/2202.13547 (2022) - [i3]Jian Kang, Qinghai Zhou, Hanghang Tong:
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks. CoRR abs/2210.05959 (2022) - 2021
- [j1]Meijia Wang, Jian Kang, Nan Cao, Yinglong Xia, Wei Fan, Hanghang Tong:
Graph Ranking Auditing: Problem Definition and Fast Solutions. IEEE Trans. Knowl. Data Eng. 33(10): 3366-3380 (2021) - [c6]Jian Kang, Hanghang Tong:
Fair Graph Mining. CIKM 2021: 4849-4852 - [c5]Yushun Dong, Jian Kang, Hanghang Tong, Jundong Li:
Individual Fairness for Graph Neural Networks: A Ranking based Approach. KDD 2021: 300-310 - [i2]Jian Kang, Tiankai Xie, Xintao Wu, Ross Maciejewski, Hanghang Tong:
MultiFair: Multi-Group Fairness in Machine Learning. CoRR abs/2105.11069 (2021) - 2020
- [c4]Jian Kang, Jingrui He, Ross Maciejewski, Hanghang Tong:
InFoRM: Individual Fairness on Graph Mining. KDD 2020: 379-389
2010 – 2019
- 2019
- [c3]Jian Kang, Hanghang Tong:
N2N: Network Derivative Mining. CIKM 2019: 861-870 - 2018
- [c2]Jian Kang, Meijia Wang, Nan Cao, Yinglong Xia, Wei Fan, Hanghang Tong:
AURORA: Auditing PageRank on Large Graphs. IEEE BigData 2018: 713-722 - [c1]Jian Kang, Scott Freitas, Haichao Yu, Yinglong Xia, Nan Cao, Hanghang Tong:
X-Rank: Explainable Ranking in Complex Multi-Layered Networks. CIKM 2018: 1959-1962 - [i1]Jian Kang, Hanghang Tong, Yinglong Xia, Wei Fan:
AURORA: Auditing PageRank on Large Graphs. CoRR abs/1803.05068 (2018)
Coauthor Index
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