Kaize Ding

According to our database, Kaize Ding authored at least 84 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Toward Robust Graph Semi-Supervised Learning Against Extreme Data Scarcity.

Kaize Ding, Elnaz Nouri, Guoqing Zheng, Huan Liu, Ryen White

IEEE Trans. Neural Networks Learn. Syst., September, 2024

Robust Graph Meta-Learning for Weakly Supervised Few-Shot Node Classification.

Kaize Ding, Jianling Wang, Jundong Li, James Caverlee, Huan Liu

ACM Trans. Knowl. Discov. Data, May, 2024

Generalized few-shot node classification: toward an uncertainty-based solution.

Zhe Xu, Kaize Ding, Yu-Xiong Wang, Huan Liu, Hanghang Tong

Knowl. Inf. Syst., February, 2024

Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey.

Ruiyao Xu, Kaize Ding

CoRR, 2024

Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models.

Qingcheng Zeng, Mingyu Jin, Qinkai Yu, Zhenting Wang, Wenyue Hua, Zihao Zhou, Guangyan Sun, Yanda Meng, Shiqing Ma, Qifan Wang, Felix Juefei-Xu, Kaize Ding, Fan Yang, Ruixiang Tang, Yongfeng Zhang

CoRR, 2024

Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark.

Yili Wang, Yixin Liu, Xu Shen, Chenyu Li, Kaize Ding, Rui Miao, Ying Wang, Shirui Pan, Xin Wang

CoRR, 2024

TSI-Bench: Benchmarking Time Series Imputation.

Wenjie Du, Jun Wang, Linglong Qian, Yiyuan Yang, Fanxing Liu, Zepu Wang, Zina M. Ibrahim, Haoxin Liu, Zhiyuan Zhao, Yingjie Zhou, Wenjia Wang, Kaize Ding, Yuxuan Liang, B. Aditya Prakash, Qingsong Wen

CoRR, 2024

Avoiding Copyright Infringement via Machine Unlearning.

Guangyao Dou, Zheyuan Liu, Qing Lyu, Kaize Ding, Eric Wong

CoRR, 2024

Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers?

Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang

CoRR, 2024

Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs.

Shuhan Liu, Kaize Ding

CoRR, 2024

Multitask Active Learning for Graph Anomaly Detection.

Wenjing Chang, Kay Liu, Kaize Ding, Philip S. Yu, Jianjun Yu

CoRR, 2024

The 5th International Workshop on Machine Learning on Graphs (MLoG).

Tyler Derr, Yao Ma, Kaize Ding, Tong Zhao, Nesreen K. Ahmed

Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders.

Zhangsihao Yang, Kaize Ding, Huan Liu, Yalin Wang

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

RelKD 2024: The Second International Workshop on Resource-Efficient Learning for Knowledge Discovery.

Chuxu Zhang, Dongkuan Xu, Kaize Ding, Jundong Li, Mojan Javaheripi, Subhabrata Mukherjee, Nitesh V. Chawla, Huan Liu

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization.

Haohui Wang, Baoyu Jing, Kaize Ding, Yada Zhu, Wei Cheng, Si Zhang, Yonghui Fan, Liqing Zhang, Dawei Zhou

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Divide and Denoise: Empowering Simple Models for Robust Semi-Supervised Node Classification against Label Noise.

Kaize Ding, Xiaoxiao Ma, Yixin Liu, Shirui Pan

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Graph Anomaly Detection with Few Labels: A Data-Centric Approach.

Xiaoxiao Ma, Ruikun Li, Fanzhen Liu, Kaize Ding, Jian Yang, Jia Wu

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning.

Zhengyu Hu, Yichuan Li, Zhengyu Chen, Jingang Wang, Han Liu, Kyumin Lee, Kaize Ding

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

On Fake News Detection with LLM Enhanced Semantics Mining.

Xiaoxiao Ma, Yuchen Zhang, Kaize Ding, Jian Yang, Jia Wu, Hao Fan

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection.

Xiongxiao Xu, Kaize Ding, Canyu Chen, Kai Shu

Proceedings of the 11th IEEE International Conference on Data Science and Advanced Analytics, 2024

Data Quality-aware Graph Machine Learning.

Yu Wang, Kaize Ding, Xiaorui Liu, Jian Kang, Ryan A. Rossi, Tyler Derr

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

Empowering Large Language Models for Textual Data Augmentation.

Yichuan Li, Kaize Ding, Jianling Wang, Kyumin Lee

Proceedings of the Findings of the Association for Computational Linguistics, 2024

Sterling: Synergistic Representation Learning on Bipartite Graphs.

Baoyu Jing, Yuchen Yan, Kaize Ding, Chanyoung Park, Yada Zhu, Huan Liu, Hanghang Tong

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Data-Efficient Graph Learning.

Kaize Ding

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

UPREVE: An End-to-End Causal Discovery Benchmarking System.

Suraj Jyothi Unni, Paras Sheth, Kaize Ding, Huan Liu, K. Selçuk Candan

CoRR, 2023

Uncertainty-Aware Robust Learning on Noisy Graphs.

Shuyi Chen, Kaize Ding, Shixiang Zhu

CoRR, 2023

MetaGAD: Learning to Meta Transfer for Few-shot Graph Anomaly Detection.

Xiongxiao Xu, Kaize Ding, Canyu Chen, Kai Shu

CoRR, 2023

Characterizing Long-Tail Categories on Graphs.

Haohui Wang, Baoyu Jing, Kaize Ding, Yada Zhu, Dawei Zhou

CoRR, 2023

Few-shot Node Classification with Extremely Weak Supervision.

Song Wang, Yushun Dong, Kaize Ding, Chen Chen, Jundong Li

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023

GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection.

Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023

HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer.

Kaize Ding, Albert Jiongqian Liang, Bryan Perozzi, Ting Chen, Ruoxi Wang, Lichan Hong, Ed H. Chi, Huan Liu, Derek Zhiyuan Cheng

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation.

Zhangsihao Yang, Mengwei Ren, Kaize Ding, Guido Gerig, Yalin Wang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Towards Self-Interpretable Graph-Level Anomaly Detection.

Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Federated Few-shot Learning.

Song Wang, Xingbo Fu, Kaize Ding, Chen Chen, Huiyuan Chen, Jundong Li

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Virtual Node Tuning for Few-shot Node Classification.

Zhen Tan, Ruocheng Guo, Kaize Ding, Huan Liu

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Learning Strong Graph Neural Networks with Weak Information.

Yixin Liu, Kaize Ding, Jianling Wang, Vincent C. S. Lee, Huan Liu, Shirui Pan

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs.

Yichuan Li, Kaize Ding, Kyumin Lee

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Learning Node Abnormality with Weak Supervision.

Qinghai Zhou, Kaize Ding, Huan Liu, Hanghang Tong

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

STREAMS: Towards Spatio-Temporal Causal Discovery with Reinforcement Learning for Streamflow Rate Prediction.

Paras Sheth, Ahmadreza Mosallanezhad, Kaize Ding, Reepal Shah, John Sabo, Huan Liu, K. Selçuk Candan

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning.

Kaize Ding, Yancheng Wang, Yingzhen Yang, Huan Liu

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Cross-Domain Graph Anomaly Detection.

Kaize Ding, Kai Shu, Xuan Shan, Jundong Li, Huan Liu

IEEE Trans. Neural Networks Learn. Syst., 2022

Data Augmentation for Deep Graph Learning: A Survey.

Kaize Ding, Zhe Xu, Hanghang Tong, Huan Liu

SIGKDD Explor., 2022

Learning with Few Labeled Nodes via Augmented Graph Self-Training.

Kaize Ding, Elnaz Nouri, Guoqing Zheng, Huan Liu, Ryen White

CoRR, 2022

Benchmarking Node Outlier Detection on Graphs.

Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, Philip S. Yu

CoRR, 2022

PyGOD: A Python Library for Graph Outlier Detection.

Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, George H. Chen, Zhihao Jia, Philip S. Yu

CoRR, 2022

A Simple Yet Effective Pretraining Strategy for Graph Few-shot Learning.

Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu

CoRR, 2022

Few-Shot Learning on Graphs: A Survey.

Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu

CoRR, 2022

Structural and Semantic Contrastive Learning for Self-supervised Node Representation Learning.

Kaize Ding, Yancheng Wang, Yingzhen Yang, Huan Liu

CoRR, 2022

Graph Few-shot Class-incremental Learning.

Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu

Proceedings of the WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21, 2022

Graph Minimally-supervised Learning.

Kaize Ding, Jundong Li, Nitesh V. Chawla, Huan Liu

Proceedings of the WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21, 2022

Causal Disentanglement with Network Information for Debiased Recommendations.

Paras Sheth, Ruocheng Guo, Kaize Ding, Lu Cheng, K. Selçuk Candan, Huan Liu

Proceedings of the Similarity Search and Applications - 15th International Conference, 2022

Classifying COVID-19 Related Meta Ads Using Discourse Representation Through a Hypergraph.

Ujun Jeong, Zeyad Alghamdi, Kaize Ding, Lu Cheng, Baoxin Li, Huan Liu

Proceedings of the Social, Cultural, and Behavioral Modeling, 2022

Supervised Graph Contrastive Learning for Few-Shot Node Classification.

Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs.

Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, Philip S. Yu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification.

Zhen Tan, Song Wang, Kaize Ding, Jundong Li, Huan Liu

Proceedings of the Learning on Graphs Conference, 2022

Task-Adaptive Few-shot Node Classification.

Song Wang, Kaize Ding, Chuxu Zhang, Chen Chen, Jundong Li

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Toward Graph Minimally-Supervised Learning.

Kaize Ding, Chuxu Zhang, Jie Tang, Nitesh V. Chawla, Huan Liu

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Few-Shot Learning on Graphs.

Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Generalized Few-Shot Node Classification.

Zhe Xu, Kaize Ding, Yu-Xiong Wang, Huan Liu, Hanghang Tong

Proceedings of the IEEE International Conference on Data Mining, 2022

Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks.

Ujun Jeong, Kaize Ding, Lu Cheng, Ruocheng Guo, Kai Shu, Huan Liu

Proceedings of the IEEE International Conference on Big Data, 2022

Meta Propagation Networks for Graph Few-shot Semi-supervised Learning.

Kaize Ding, Jianling Wang, James Caverlee, Huan Liu

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Weakly-supervised Graph Meta-learning for Few-shot Node Classification.

Kaize Ding, Jianling Wang, Jundong Li, James Caverlee, Huan Liu

CoRR, 2021

FBAdTracker: An Interactive Data Collection and Analysis Tool for Facebook Advertisements.

Ujun Jeong, Kaize Ding, Huan Liu

CoRR, 2021

Graph Neural Networks with Adaptive Frequency Response Filter.

Yushun Dong, Kaize Ding, Brian Jalaian, Shuiwang Ji, Jundong Li

CoRR, 2021

Few-shot Network Anomaly Detection via Cross-network Meta-learning.

Kaize Ding, Qinghai Zhou, Hanghang Tong, Huan Liu

Proceedings of the WWW '21: The Web Conference 2021, 2021

Sequential Recommendation for Cold-start Users with Meta Transitional Learning.

Jianling Wang, Kaize Ding, James Caverlee

Proceedings of the SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021

Session-based Recommendation with Hypergraph Attention Networks.

Jianling Wang, Kaize Ding, Ziwei Zhu, James Caverlee

Proceedings of the 2021 SIAM International Conference on Data Mining, 2021

Learning to Selectively Learn for Weakly-supervised Paraphrase Generation.

Kaize Ding, Dingcheng Li, Alexander Hanbo Li, Xing Fan, Chenlei Guo, Yang Liu, Huan Liu

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter.

Yushun Dong, Kaize Ding, Brian Jalaian, Shuiwang Ji, Jundong Li

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

Towards Anomaly-resistant Graph Neural Networks via Reinforcement Learning.

Kaize Ding, Xuan Shan, Huan Liu

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

GLOW : Global Weighted Self-Attention Network for Web Search.

Xuan Shan, Chuanjie Liu, Yiqian Xia, Qi Chen, Yusi Zhang, Kaize Ding, Yaobo Liang, Angen Luo, Yuxiang Luo

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Fact-Enhanced Synthetic News Generation.

Kai Shu, Yichuan Li, Kaize Ding, Huan Liu

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Combating disinformation in a social media age.

Kai Shu, Amrita Bhattacharjee, Faisal Alatawi, Tahora H. Nazer, Kaize Ding, Mansooreh Karami, Huan Liu

WIREs Data Mining Knowl. Discov., 2020

Key Opinion Leaders in Recommendation Systems: Opinion Elicitation and Diffusion.

Jianling Wang, Kaize Ding, Ziwei Zhu, Yin Zhang, James Caverlee

Proceedings of the WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining, 2020

Next-item Recommendation with Sequential Hypergraphs.

Jianling Wang, Kaize Ding, Liangjie Hong, Huan Liu, James Caverlee

Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020

Inductive Anomaly Detection on Attributed Networks.

Kaize Ding, Jundong Li, Nitin Agarwal, Huan Liu

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Be More with Less: Hypergraph Attention Networks for Inductive Text Classification.

Kaize Ding, Jianling Wang, Jundong Li, Dingcheng Li, Huan Liu

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

Graph Few-shot Learning with Attribute Matching.

Ning Wang, Minnan Luo, Kaize Ding, Lingling Zhang, Jundong Li, Qinghua Zheng

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

Graph Prototypical Networks for Few-shot Learning on Attributed Networks.

Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, Huan Liu

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

Challenges in Combating COVID-19 Infodemic - Data, Tools, and Ethics.

Kaize Ding, Kai Shu, Yichuan Li, Amrita Bhattacharjee, Huan Liu

Proceedings of the CIKM 2020 Workshops co-located with 29th ACM International Conference on Information and Knowledge Management (CIKM 2020), 2020

2019

Graph Neural Networks with High-order Feature Interactions.

Kaize Ding, Yichuan Li, Jundong Li, Chenghao Liu, Huan Liu

CoRR, 2019

Interactive Anomaly Detection on Attributed Networks.

Kaize Ding, Jundong Li, Huan Liu

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019

Deep Anomaly Detection on Attributed Networks.

Kaize Ding, Jundong Li, Rohit Bhanushali, Huan Liu

Proceedings of the 2019 SIAM International Conference on Data Mining, 2019

InterSpot: Interactive Spammer Detection in Social Media.

Kaize Ding, Jundong Li, Shivam Dhar, Shreyash Devan, Huan Liu

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019