InterSpot: Interactive Spammer Detection in Social Media. - Kaize Ding; Jundong Li; Shivam Dhar; Shreyash Devan; Huan Liu 001 (IJCAI, 2019)

Deep Anomaly Detection on Attributed Networks. - Kaize Ding; Jundong Li; Rohit Bhanushali; Huan Liu 001 (SDM, 2019)

Interactive Anomaly Detection on Attributed Networks. - Kaize Ding; Jundong Li; Huan Liu 001 (WSDM, 2019)

Graph Neural Networks with High-order Feature Interactions. - Kaize Ding; Yichuan Li 001; Jundong Li; Chenghao Liu; Huan Liu 001 (CoRR, 2019)

Challenges in Combating COVID-19 Infodemic - Data, Tools, and Ethics. - Kaize Ding; Kai Shu; Yichuan Li 001; Amrita Bhattacharjee; Huan Liu 001 (CIKM (Workshops), 2020)

Graph Prototypical Networks for Few-shot Learning on Attributed Networks. - Kaize Ding; Jianling Wang; Jundong Li; Kai Shu; Chenghao Liu; Huan Liu 001 (CIKM, 2020)

Graph Few-shot Learning with Attribute Matching. - Ning Wang 020; Minnan Luo; Kaize Ding; Lingling Zhang 005; Jundong Li; Qinghua Zheng (CIKM, 2020)

Be More with Less: Hypergraph Attention Networks for Inductive Text Classification. - Kaize Ding; Jianling Wang; Jundong Li; Dingcheng Li; Huan Liu 001 (EMNLP (1), 2020)

Inductive Anomaly Detection on Attributed Networks. - Kaize Ding; Jundong Li; Nitin Agarwal 001; Huan Liu 001 (IJCAI, 2020)

Next-item Recommendation with Sequential Hypergraphs. - Jianling Wang; Kaize Ding; Liangjie Hong; Huan Liu 001; James Caverlee (SIGIR, 2020)

Key Opinion Leaders in Recommendation Systems: Opinion Elicitation and Diffusion. - Jianling Wang; Kaize Ding; Ziwei Zhu 001; Yin Zhang 011; James Caverlee (WSDM, 2020)

Combating disinformation in a social media age. - Kai Shu; Amrita Bhattacharjee; Faisal Alatawi; Tahora H. Nazer; Kaize Ding; Mansooreh Karami; Huan Liu 001 (WIREs Data Mining Knowl. Discov., 2020)

Fact-Enhanced Synthetic News Generation. - Kai Shu; Yichuan Li 001; Kaize Ding; Huan Liu 001 (AAAI, 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 (IEEE BigData, 2021)

Towards Anomaly-resistant Graph Neural Networks via Reinforcement Learning. - Kaize Ding; Xuan Shan; Huan Liu 001 (CIKM, 2021)

AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter. - Yushun Dong; Kaize Ding; Brian Jalaian; Shuiwang Ji; Jundong Li (CIKM, 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 001 (EMNLP (1), 2021)

Session-based Recommendation with Hypergraph Attention Networks. - Jianling Wang; Kaize Ding; Ziwei Zhu 001; James Caverlee (SDM, 2021)

Sequential Recommendation for Cold-start Users with Meta Transitional Learning. - Jianling Wang; Kaize Ding; James Caverlee (SIGIR, 2021)

Few-shot Network Anomaly Detection via Cross-network Meta-learning. - Kaize Ding; Qinghai Zhou; Hanghang Tong; Huan Liu 001 (WWW, 2021)

Graph Neural Networks with Adaptive Frequency Response Filter. - Yushun Dong; Kaize Ding; Brian Jalaian; Shuiwang Ji; Jundong Li (CoRR, 2021)

FBAdTracker: An Interactive Data Collection and Analysis Tool for Facebook Advertisements. - Ujun Jeong; Kaize Ding; Huan Liu 001 (CoRR, 2021)

Weakly-supervised Graph Meta-learning for Few-shot Node Classification. - Kaize Ding; Jianling Wang; Jundong Li; James Caverlee; Huan Liu 001 (CoRR, 2021)

Meta Propagation Networks for Graph Few-shot Semi-supervised Learning. - Kaize Ding; Jianling Wang; James Caverlee; Huan Liu 001 (AAAI, 2022)

Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks. - Ujun Jeong; Kaize Ding; Lu Cheng 001; Ruocheng Guo; Kai Shu; Huan Liu 001 (IEEE Big Data, 2022)

Generalized Few-Shot Node Classification. - Zhe Xu 007; Kaize Ding; Yu-Xiong Wang; Huan Liu 001; Hanghang Tong (ICDM, 2022)

Few-Shot Learning on Graphs. - Chuxu Zhang; Kaize Ding; Jundong Li; Xiangliang Zhang 001; Yanfang Ye 001; Nitesh V. Chawla; Huan Liu 001 (IJCAI, 2022)

Toward Graph Minimally-Supervised Learning. - Kaize Ding; Chuxu Zhang; Jie Tang 001; Nitesh V. Chawla; Huan Liu 001 (KDD, 2022)

Task-Adaptive Few-shot Node Classification. - Song Wang 013; Kaize Ding; Chuxu Zhang; Chen Chen 022; Jundong Li (KDD, 2022)

Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification. - Zhen Tan 001; Song Wang 013; Kaize Ding; Jundong Li; Huan Liu 001 (LoG, 2022)

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs. - Kay Liu; Yingtong Dou; Yue Zhao 016; Xueying Ding; Xiyang Hu; Ruitong Zhang; Kaize Ding; Canyu Chen; Hao Peng 001; Kai Shu; Lichao Sun 001; Jundong Li; et al. (NeurIPS, 2022)

Supervised Graph Contrastive Learning for Few-Shot Node Classification. - Zhen Tan 001; Kaize Ding; Ruocheng Guo; Huan Liu 001 (ECML/PKDD (2), 2022)

Classifying COVID-19 Related Meta Ads Using Discourse Representation Through a Hypergraph. - Ujun Jeong; Zeyad Alghamdi; Kaize Ding; Lu Cheng 001; Baoxin Li; Huan Liu 001 (SBP-BRiMS, 2022)

Causal Disentanglement with Network Information for Debiased Recommendations. - Paras Sheth; Ruocheng Guo; Kaize Ding; Lu Cheng 001; K. Selçuk Candan; Huan Liu 001 (SISAP, 2022)

Graph Minimally-supervised Learning. - Kaize Ding; Jundong Li; Nitesh V. Chawla; Huan Liu 001 (WSDM, 2022)

Graph Few-shot Class-incremental Learning. - Zhen Tan 001; Kaize Ding; Ruocheng Guo; Huan Liu 001 (WSDM, 2022)

Structural and Semantic Contrastive Learning for Self-supervised Node Representation Learning. - Kaize Ding; Yancheng Wang; Yingzhen Yang; Huan Liu 001 (CoRR, 2022)

Few-Shot Learning on Graphs: A Survey. - Chuxu Zhang; Kaize Ding; Jundong Li; Xiangliang Zhang 001; Yanfang Ye 001; Nitesh V. Chawla; Huan Liu 001 (CoRR, 2022)

A Simple Yet Effective Pretraining Strategy for Graph Few-shot Learning. - Zhen Tan 001; Kaize Ding; Ruocheng Guo; Huan Liu 001 (CoRR, 2022)

PyGOD: A Python Library for Graph Outlier Detection. - Kay Liu; Yingtong Dou; Yue Zhao 016; Xueying Ding; Xiyang Hu; Ruitong Zhang; Kaize Ding; Canyu Chen; Hao Peng 001; Kai Shu; George H. Chen; Zhihao Jia; et al. (CoRR, 2022)

Benchmarking Node Outlier Detection on Graphs. - Kay Liu; Yingtong Dou; Yue Zhao 016; Xueying Ding; Xiyang Hu; Ruitong Zhang; Kaize Ding; Canyu Chen; Hao Peng 001; Kai Shu; Lichao Sun 001; Jundong Li; et al. (CoRR, 2022)

Learning with Few Labeled Nodes via Augmented Graph Self-Training. - Kaize Ding; Elnaz Nouri; Guoqing Zheng; Huan Liu 001; Ryen White (CoRR, 2022)

Data Augmentation for Deep Graph Learning: A Survey. - Kaize Ding; Zhe Xu 007; Hanghang Tong; Huan Liu 001 (SIGKDD Explor., 2022)

Cross-Domain Graph Anomaly Detection. - Kaize Ding; Kai Shu; Xuan Shan; Jundong Li; Huan Liu 001 (IEEE Trans. Neural Networks Learn. Syst., 2022)

Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning. - Kaize Ding; Yancheng Wang; Yingzhen Yang; Huan Liu 001 (AAAI, 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 001; K. Selçuk Candan (CIKM, 2023)

Learning Node Abnormality with Weak Supervision. - Qinghai Zhou; Kaize Ding; Huan Liu 001; Hanghang Tong (CIKM, 2023)

GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs. - Yichuan Li 001; Kaize Ding; Kyumin Lee (EMNLP (Findings), 2023)

Learning Strong Graph Neural Networks with Weak Information. - Yixin Liu 001; Kaize Ding; Jianling Wang; Vincent C. S. Lee; Huan Liu 001; Shirui Pan (KDD, 2023)

Virtual Node Tuning for Few-shot Node Classification. - Zhen Tan 001; Ruocheng Guo; Kaize Ding; Huan Liu 001 (KDD, 2023)

Federated Few-shot Learning. - Song Wang 013; Xingbo Fu; Kaize Ding; Chen Chen 022; Huiyuan Chen; Jundong Li (KDD, 2023)

Towards Self-Interpretable Graph-Level Anomaly Detection. - Yixin Liu 001; Kaize Ding; Qinghua Lu 001; Fuyi Li; Leo Yu Zhang; Shirui Pan (NeurIPS, 2023)

Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation. - Zhangsihao Yang; Mengwei Ren; Kaize Ding; Guido Gerig; Yalin Wang 001 (NeurIPS, 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 001; Derek Zhiyuan Cheng (SIGIR, 2023)

GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection. - Yixin Liu 001; Kaize Ding; Huan Liu 001; Shirui Pan (WSDM, 2023)

Few-shot Node Classification with Extremely Weak Supervision. - Song Wang 013; Yushun Dong; Kaize Ding; Chen Chen 022; Jundong Li (WSDM, 2023)

Characterizing Long-Tail Categories on Graphs. - Haohui Wang; Baoyu Jing; Kaize Ding; Yada Zhu; Dawei Zhou 003 (CoRR, 2023)

MetaGAD: Learning to Meta Transfer for Few-shot Graph Anomaly Detection. - Xiongxiao Xu; Kaize Ding; Canyu Chen; Kai Shu (CoRR, 2023)

UPREVE: An End-to-End Causal Discovery Benchmarking System. - Suraj Jyothi Unni; Paras Sheth; Kaize Ding; Huan Liu 001; K. Selçuk Candan (CoRR, 2023)

Data-Efficient Graph Learning. - Kaize Ding (AAAI, 2024)

Sterling: Synergistic Representation Learning on Bipartite Graphs. - Baoyu Jing; Yuchen Yan; Kaize Ding; Chanyoung Park 001; Yada Zhu; Huan Liu 001; Hanghang Tong (AAAI, 2024)

Empowering Large Language Models for Textual Data Augmentation. - Yichuan Li 001; Kaize Ding; Jianling Wang; Kyumin Lee (ACL (Findings), 2024)

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

MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection. - Xiongxiao Xu; Kaize Ding; Canyu Chen; Kai Shu (DSAA, 2024)

On Fake News Detection with LLM Enhanced Semantics Mining. - Xiaoxiao Ma 002; Yuchen Zhang; Kaize Ding; Jian Yang 001; Jia Wu 001; Hao Fan 003 (EMNLP, 2024)

Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning. - Zhengyu Hu; Yichuan Li 001; Zhengyu Chen 001; Jingang Wang; Han Liu; Kyumin Lee; Kaize Ding (EMNLP (Findings), 2024)

Graph Anomaly Detection with Few Labels: A Data-Centric Approach. - Xiaoxiao Ma 002; Ruikun Li 001; Fanzhen Liu; Kaize Ding; Jian Yang 001; Jia Wu 001 (KDD, 2024)

Divide and Denoise: Empowering Simple Models for Robust Semi-Supervised Node Classification against Label Noise. - Kaize Ding; Xiaoxiao Ma 002; Yixin Liu 001; Shirui Pan (KDD, 2024)

Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization. - Haohui Wang; Baoyu Jing; Kaize Ding; Yada Zhu; Wei Cheng 002; Si Zhang; Yonghui Fan; Liqing Zhang 002; Dawei Zhou 003 (KDD, 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 001 (KDD, 2024)

Revisiting Score Propagation in Graph Out-of-Distribution Detection. - Longfei Ma; Yiyou Sun; Kaize Ding; Zemin Liu; Fei Wu 001 (NeurIPS, 2024)

MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders. - Zhangsihao Yang; Kaize Ding; Huan Liu 001; Yalin Wang 001 (WACV, 2024)

The 5th International Workshop on Machine Learning on Graphs (MLoG). - Tyler Derr; Yao Ma 001; Kaize Ding; Tong Zhao 003; Nesreen K. Ahmed (WSDM, 2024)

Data-efficient graph learning: Problems, progress, and prospects. - Kaize Ding; Yixin Liu 001; Chuxu Zhang; Jianling Wang (AI Mag., 2024)

Multitask Active Learning for Graph Anomaly Detection. - Wenjing Chang; Kay Liu; Kaize Ding; Philip S. Yu; Jianjun Yu (CoRR, 2024)

Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs. - Shuhan Liu; Kaize Ding (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 003; Kai Mei; Yanda Meng; Kaize Ding; Fan Yang 023; Mengnan Du; et al. (CoRR, 2024)

Avoiding Copyright Infringement via Machine Unlearning. - Guangyao Dou; Zheyuan Liu 010; Qing Lyu 001; Kaize Ding; Eric Wong 001 (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 001; Zhiyuan Zhao 002; Yingjie Zhou; Wenjia Wang; Kaize Ding; et al. (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 001; Felix Juefei-Xu; Kaize Ding; et al. (CoRR, 2024)

LEGO-Learn: Label-Efficient Graph Open-Set Learning. - Haoyan Xu; Kay Liu; Zhengtao Yao; Philip S. Yu; Kaize Ding; Yue Zhao 016 (CoRR, 2024)

A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation. - Kexin Zhang 007; Shuhan Liu; Song Wang 013; Weili Shi; Chen Chen 022; Pan Li; Sheng Li 001; Jundong Li; Kaize Ding (CoRR, 2024)

Political-LLM: Large Language Models in Political Science. - Lincan Li; Jiaqi Li; Catherine Chen 003; Fred Gui; Hongjia Yang; Chenxiao Yu; Zhengguang Wang; Jianing Cai; Junlong Aaron Zhou; Bolin Shen; Alex Qian; Weixin Chen; et al. (CoRR, 2024)

AD-LLM: Benchmarking Large Language Models for Anomaly Detection. - Tiankai Yang 001; Yi Nian; Shawn Li; Ruiyao Xu; Yuangang Li 002; Jiaqi Li; Zhuo Xiao; Xiyang Hu; Ryan A. Rossi; Kaize Ding; Xiaohu You 001; Yue Zhao 016 (CoRR, 2024)

RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response. - Junyu Luo 002; Xiao Luo 001; Kaize Ding; Jingyang Yuan; Zhiping Xiao 001; Ming Zhang 004 (CoRR, 2024)

Generalized few-shot node classification: toward an uncertainty-based solution. - Zhe Xu 007; Kaize Ding; Yu-Xiong Wang; Huan Liu 001; Hanghang Tong (Knowl. Inf. Syst., 2024)

Robust Graph Meta-Learning for Weakly Supervised Few-Shot Node Classification. - Kaize Ding; Jianling Wang; Jundong Li; James Caverlee; Huan Liu 001 (ACM Trans. Knowl. Discov. Data, 2024)

Toward Robust Graph Semi-Supervised Learning Against Extreme Data Scarcity. - Kaize Ding; Elnaz Nouri; Guoqing Zheng; Huan Liu 001; Ryen White (IEEE Trans. Neural Networks Learn. Syst., 2024)

AD-LLM: Benchmarking Large Language Models for Anomaly Detection. - Tiankai Yang 001; Yi Nian; Li Li 006; Ruiyao Xu; Yuangang Li 002; Jiaqi Li; Zhuo Xiao; Xiyang Hu; Ryan A. Rossi; Kaize Ding; Xia Hu 001; Yue Zhao 016 (ACL (Findings), 2025)

Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? - Mingyu Jin; Qinkai Yu; Jingyuan Huang; Qingcheng Zeng; Zhenting Wang; Wenyue Hua; Haiyan Zhao 003; Kai Mei; Yanda Meng; Kaize Ding; Fan Yang 023; Mengnan Du; et al. (COLING, 2025)

Uncertainty-Aware Robust Learning on Noisy Graphs. - Shuyi Chen; Kaize Ding; Shixiang Zhu (ICASSP, 2025)

Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark. - Yili Wang 004; Yixin Liu 001; Xu Shen 002; Chenyu Li; Rui Miao 003; Kaize Ding; Ying Wang 009; Shirui Pan; Xin Wang 035 (ICLR, 2025)

On Large Language Model Continual Unlearning. - Chongyang Gao; Lixu Wang; Kaize Ding; Chenkai Weng; Xiao Wang; Qi Zhu (ICLR, 2025)

Avoiding Copyright Infringement via Large Language Model Unlearning. - Guangyao Dou; Zheyuan Liu 010; Qing Lyu 001; Kaize Ding; Eric Wong 001 (NAACL (Findings), 2025)

ALERT: An LLM-powered Benchmark for Automatic Evaluation of Recommendation Explanations. - Yichuan Li 001; Xinyang Zhang 002; Chenwei Zhang; Mao Li; Tianyi Liu; Pei Chen; Yifan Gao 001; Kyumin Lee; Kaize Ding; Zhengyang Wang; Zhihan Zhang 001; Jingbo Shang; et al. (NAACL (Long Papers), 2025)

Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey. - Ruiyao Xu; Kaize Ding (NAACL (Findings), 2025)

Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models. - Kexin Zhang 007; Fuyuan Lyu; Xing Tang 007; Dugang Liu; Chen Ma 001; Kaize Ding; Xiuqiang He 001; Xue Liu 001 (WSDM, 2025)

Resource-Efficient Learning for the Web. - Chuxu Zhang; Kaize Ding; Jundong Li; Dongkuan Xu; Haoyu Wang 004; Derek Zhiyuan Cheng; Huan Liu 001 (WWW (Companion Volume), 2025)

RelWeb 2025: The International Workshop on Resource-Efficient Learning for the Web. - Chuxu Zhang; Kaize Ding; Jundong Li; Dongkuan Xu; Haoyu Wang 004; Derek Zhiyuan Cheng; Huan Liu 001 (WWW (Companion Volume), 2025)

LEGO-Learn: Label-Efficient Graph Open-Set Learning. - Haoyan Xu; Kay Liu; Zhengtao Yao; Philip S. Yu; Mengyuan Li; Kaize Ding; Yue Zhao 016 (Trans. Mach. Learn. Res., 2025)

A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments. - Kaixiang Zhao; Lincan Li; Kaize Ding; Neil Zhenqiang Gong; Yue Zhao 016; Yushun Dong (CoRR, 2025)

Topology-Aware Conformal Prediction for Stream Networks. - Jifan Zhang; Fangxin Wang 003; Philip S. Yu; Kaize Ding; Shixiang Zhu (CoRR, 2025)

A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization. - Ziqing Wang; Kexin Zhang 007; Zihan Zhao; Yibo Wen; Abhishek Pandey; Han Liu; Kaize Ding (CoRR, 2025)

Cross-Domain Conditional Diffusion Models for Time Series Imputation. - Kexin Zhang 007; Baoyu Jing; K. Selçuk Candan; Dawei Zhou 003; Qingsong Wen; Han Liu; Kaize Ding (CoRR, 2025)

A Survey on Model Extraction Attacks and Defenses for Large Language Models. - Kaixiang Zhao; Lincan Li; Kaize Ding; Neil Zhenqiang Gong; Yue Zhao 016; Yushun Dong (CoRR, 2025)

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives. - Kaixiang Zhao; Lincan Li; Kaize Ding; Neil Zhenqiang Gong; Yue Zhao 016; Yushun Dong (CoRR, 2025)