| Inform: Individual fairness on graph mining J Kang, J He, R Maciejewski, H Tong Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 155 | 2020 |
| Do we really need complicated model architectures for temporal networks? W Cong, S Zhang, J Kang, B Yuan, H Wu, X Zhou, H Tong, M Mahdavi arXiv preprint arXiv:2302.11636, 2023 | 145 | 2023 |
| Individual fairness for graph neural networks: A ranking based approach Y Dong, J Kang, H Tong, J Li Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 133 | 2021 |
| RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network J Kang, Y Zhu, Y Xia, J Luo, H Tong Proceedings of the ACM Web Conference 2022, 2022 | 64 | 2022 |
| Infofair: Information-theoretic intersectional fairness J Kang, T Xie, X Wu, R Maciejewski, H Tong 2022 IEEE International Conference on Big Data (Big Data), 1455-1464, 2022 | 48* | 2022 |
| Fair graph mining J Kang, H Tong Proceedings of the 30th ACM International Conference on Information …, 2021 | 36 | 2021 |
| Fairrankvis: A visual analytics framework for exploring algorithmic fairness in graph mining models T Xie, Y Ma, J Kang, H Tong, R Maciejewski IEEE transactions on visualization and computer graphics 28 (1), 368-377, 2021 | 35 | 2021 |
| JuryGCN: quantifying jackknife uncertainty on graph convolutional networks J Kang, Q Zhou, H Tong Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 23 | 2022 |
| N2n: Network derivative mining J Kang, H Tong Proceedings of the 28th ACM International Conference on Information and …, 2019 | 19 | 2019 |
| IMBENS: Ensemble class-imbalanced learning in Python Z Liu, J Kang, H Tong, Y Chang arXiv preprint arXiv:2111.12776, 2021 | 17 | 2021 |
| Aurora: Auditing pagerank on large graphs J Kang, M Wang, N Cao, Y Xia, W Fan, H Tong 2018 IEEE International Conference on Big Data (Big Data), 713-722, 2018 | 17 | 2018 |
| Bemap: Balanced message passing for fair graph neural network X Lin, J Kang, W Cong, H Tong Learning on Graphs Conference, 37: 1-37: 25, 2024 | 15 | 2024 |
| Ensuring user-side fairness in dynamic recommender systems H Yoo, Z Zeng, J Kang, R Qiu, D Zhou, Z Liu, F Wang, C Xu, E Chan, ... Proceedings of the ACM Web Conference 2024, 3667-3678, 2024 | 14 | 2024 |
| X-rank: Explainable ranking in complex multi-layered networks J Kang, S Freitas, H Yu, Y Xia, N Cao, H Tong Proceedings of the 27th ACM International Conference on Information and …, 2018 | 12 | 2018 |
| Graph Ranking Auditing: Problem Definition and Fast Solutions M Wang, J Kang, N Cao, Y Xia, W Fan, H Tong IEEE Transactions on Knowledge and Data Engineering, 2020 | 11 | 2020 |
| Conformalized link prediction on graph neural networks T Zhao, J Kang, L Cheng Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024 | 8 | 2024 |
| ifig: Individually fair multi-view graph clustering Y Wang, J Kang, Y Xia, J Luo, H Tong 2022 IEEE International Conference on Big Data (Big Data), 329-338, 2022 | 6 | 2022 |
| Algorithmic fairness on graphs: Methods and trends J Kang, H Tong Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 6 | 2022 |
| Deceptive fairness attacks on graphs via meta learning J Kang, Y Xia, R Maciejewski, J Luo, H Tong arXiv preprint arXiv:2310.15653, 2023 | 5 | 2023 |
| On the sensitivity of individual fairness: Measures and robust algorithms X He, J Kang, R Qiu, F Wang, J Sepulveda, H Tong Proceedings of the 33rd ACM International Conference on Information and …, 2024 | 4 | 2024 |
Hanghang TongUniversity of Illinois at Urbana-ChampaignPreverjeni e-poštni naslov na illinois.edu