Tutorials

  1. Data-Efficient Graph Learning [proposal] [website]
    Kaize Ding, Jundong Li, Chuxu Zhang, Jie Tang, and Huan Liu
    SIAM International Conference on Data Mining (SDM), 2023
  2. Augmentation Methods for Graph Learning [proposal] [website]
    Tong Zhao, Kaize Ding, Wei Jin, Gang Liu, Meng Jiang and Neil Shah
    SIAM International Conference on Data Mining (SDM), 2023
  3. Toward Graph Minimally-Supervised Learning [proposal] [website]
    Kaize Ding, Chuxu Zhang, Jie Tang, Nitesh V. Chawla, and Huan Liu
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022
  4. Graph Minimally-supervised Learning [proposal] [website]
    Kaize Ding, Jundong Li, Nitesh V. Chawla, and Huan Liu
    ACM International Conference on Web Search and Data Mining (WSDM), 2022

Workshops

  1. The 5th International Workshop on Machine Learning on Graphs (MLoG) [website]
    Tyler Derr, Yao Ma, Kaize Ding, Tong Zhao, and Nesreen K. Ahmed

Refereed Papers

2024
  1. Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity [pdf] [code]
    Kaize Ding, Elnaz Nouri, Guoqing Zheng, Huan Liu, and Ryen White
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2024
  2. Empowering Large Language Models for Textual Data Augmentation [pdf] [code]
    Yichuan Li*, Kaize Ding*, Jianling Wang and Kyumin Lee (*equal contribution)
    ICLR 2024 Workshop on Data-centric Machine Learning Research (DMLR), 2024
  3. STERLING: Synergistic Representation Learning on Bipartite Graphs [pdf] [code]
    Baoyu Jing, Yuchen Yan, Kaize Ding, Chanyoung Park, Yada Zhu, Huan Liu, and Hanghang Tong
    AAAI Conference on Artificial Intelligence (AAAI), 2024
  4. MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders [pdf] [code]
    Zhangsihao Yang, Kaize Ding, Huan Liu, and Yalin Wang
    IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024
  5. Generalized Few-Shot Node Classification: Towards an Uncertainty-Based Solution [pdf] [code]
    Zhe Xu, Kaize Ding, Yuxiong Wang, Huan Liu, and Hanghang Tong
    Knowledge and Information Systems (KAIS), 2024
2023
  1. Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning [pdf] [code]
    Kaize Ding*, Yancheng Wang*, Yingzhen Yang, and Huan Liu (*equal contribution)
    AAAI Conference on Artificial Intelligence (AAAI), 2023
  2. HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer (short paper) [pdf] [code]
    Kaize Ding, Albert Jiongqian Liang, Bryan Perrozi, Ting Chen, Ruoxi Wang, Lichan Hong, Ed Chi, Huan Liu, and Derek Zhiyuan Cheng
    ACM International SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023
  3. Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification [pdf] [code]
    Kaize Ding, Jianling Wang, Jundong Li, James Caverlee, and Huan Liu
    ACM Transactions on Knowledge Discovery from Data (TKDD), 2023
    CIKM 2022 Workshop on Trustworthy Learning on Graphs (TrustLOG), Best Paper Award
  4. GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection [pdf] [code]
    Yixin Liu*, Kaize Ding*, Huan Liu, and Shirui Pan (*equal contribution)
    ACM International Conference on Web Search and Data Mining (WSDM), 2023
  5. GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs [pdf] [code]
    Yichuan Li, Kaize Ding, and Kyumin Lee
    Conference on Empirical Methods in Natural Language Processing (Findings of EMNLP), 2023
  6. Towards Self-Interpretable Graph-Level Anomaly Detection [pdf] [code]
    Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, and Shirui Pan
    Advances in Neural Information Processing Systems (NeurIPS), 2023
  7. Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation [pdf] [code]
    Zhangsihao Yang*, Mengwei Ren*, Kaize Ding, Guido Gerig, and Yalin Wang (*equal contribution)
    Advances in Neural Information Processing Systems (NeurIPS), 2023
  8. Learning Strong Graph Neural Networks with Weak Information [pdf] [code]
    Yixin Liu, Kaize Ding, Jianling Wang, Vincent Lee, Huan Liu, and Shirui Pan
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023
  9. Learning Node Abnormality with Weak Supervision [pdf] [code]
    Qinghai Zhou, Kaize Ding, Huan Liu, and Hanghang Tong
    ACM International Conference on Information and Knowledge Management (CIKM), 2023
  10. Federated Few-shot Learning [pdf] [code]
    Song Wang, Xingbo Fu, Kaize Ding, Chen Chen, Huiyuan Chen, and Jundong Li
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023
  11. Virtual Node Tuning for Few-Shot Node Classification [pdf] [code]
    Zhen Tan, Ruocheng Guo, Kaize Ding, and Huan Liu
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023
  12. Few-shot Node Classification with Extremely Weak Supervision [pdf] [code]
    Song Wang, Yushun Dong, Kaize Ding, Chenchen, and Jundong Li
    ACM International Conference on Web Search and Data Mining (WSDM), 2023
  13. STREAMS: Towards Spatio-Temporal Causal Discovery with Reinforcement Learning for Streamflow Rate Prediction (ADS Track) [pdf] [code]
    Paras Sheth, Ahmadreza Mosallanezhad, Kaize Ding, Reepal Shah, John Sabo, Huan Liu, and K. Selçuk Candan
    ACM International Conference on Information and Knowledge Management (CIKM), 2023
  14. UPREVE: An End-to-End Causal Discovery Benchmarking System (demo paper) [pdf] [code]
    Suraj Jyothi Unni, Paras Sheth, Kaize Ding, Huan Liu, and K. Selcuk Candan
    International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), 2023
2022
  1. Data Augmentation for Deep Graph Learning: A Survey [pdf] [code]
    Kaize Ding, Zhe Xu, Hanghang Tong, and Huan Liu
    SIGKDD Explorations, 2022
  2. Meta Propagation Networks for Graph Few-shot Semi-supervised Learning [pdf] [code]
    Kaize Ding, Jianling Wang, James Caverlee, and Huan Liu
    AAAI Conference on Artificial Intelligence (AAAI), 2022
  3. Cross-domain Graph Anomaly Detection [pdf] [code]
    Kaize Ding, Kai Shu, Xuan Shan, Jundong Li, and Huan Liu
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022
  4. Transductive Linear Probing: A Novel Framework for Few-shot Node Classification [pdf] [code]
    Zhen Tan*, Song Wang*, Kaize Ding*, Jundong Li, and Huan Liu (*equal contribution)
    Learning on Graphs Conference (LoG), 2022
  5. Generalized Few-shot Node Classification [pdf] [code]
    Zhe Xu, Kaize Ding, Yuxiong Wang, Huan Liu, and Hanghang Tong
    IEEE International Conference on Data Mining (ICDM), 2022
  6. Graph Few-shot Class-incremental Learning [pdf] [code]
    Zhen Tan, Kaize Ding, Ruocheng Guo, and Huan Liu
    ACM International Conference on Web Search and Data Mining (WSDM), 2022
  7. Few-Shot Learning on Graphs: A Survey [pdf] [code]
    Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, and Huan Liu
    International Joint Conference on Artificial Intelligence (IJCAI), 2022
  8. Task-Adaptive Few-shot Node Classification [pdf] [code]
    Song Wang, Kaize Ding, Chuxu Zhang, Chen Chen, and Jundong Li
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022
  9. Supervised Graph Contrastive Learning for Few-shot Node Classification [pdf] [code]
    Zhen Tan, Kaize Ding, Ruocheng Guo, and Huan Liu
    European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML-PKDD), 2022
  10. Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks [pdf] [code]
    Ujun Jeong, Kaize Ding, Ruocheng Guo, Lu Cheng, Kai Shu, and Huan Liu
    IEEE International Conference on Big Data (BigData), 2022
  11. Benchmarking Node Outlier Detection on Graphs [pdf] [code]
    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, and Philip S. Yu (*equal contribution)
    Advances in Neural Information Processing Systems (NeurIPS), 2022
  12. Causal Disentanglement with Network Information for Debiased Recommendations [pdf] [code]
    Paras Sheth, Ruocheng Guo, Kaize Ding, Lu Cheng, K. Selcuk Candan, and Huan Liu
    International Conference on Similarity Search and Applications (SISAP), 2022
  13. Classifying COVID-19 related Meta Ads using Discourse Representation through Hypergraph [pdf] [code]
    Ujun Jeong, Zeyad Alghamdi, Kaize Ding, Lu Cheng, Baoxin Li, and Huan Liu
    International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), 2022
2021
  1. Learning to Selectively Learn for Weakly-supervised Paraphrase Generation [pdf] [code]
    Kaize Ding, Dingcheng Li, Alexander Hanbo Li, Xing Fan, Chenlei Guo, Yang Liu, and Huan Liu
    Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
  2. Few-shot Network Anomaly Detection with Cross-network Meta-learning [pdf] [code]
    Kaize Ding*, Qinghai Zhou*, Hanghang Tong, and Huan Liu (*equal contribution)
    The Web Conference (formerly WWW), 2021
  3. Towards Anomaly-resistant Graph Neural Networks via Reinforcement Learning (short paper) [pdf] [code]
    Kaize Ding, Xuan Shan, and Huan Liu
    ACM International Conference on Information and Knowledge Management (CIKM), 2021
  4. Graph Neural Networks with Adaptive Frequency Response Filter [pdf] [code]
    Yushun Dong, Kaize Ding, Brian Jalaian, Shuiwang Ji, and Jundong Li
    ACM International Conference on Information and Knowledge Management (CIKM), 2021
  5. Sequential Recommendation for Cold-start Users with Meta Transitional Learning (short paper) [pdf] [code]
    Jianling Wang, Kaize Ding, and James Caverlee
    ACM International SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2021
  6. Fact-enhanced Synthetic News Generation [pdf] [code]
    Kai Shu*, Yichuan Li*, Kaize Ding, and Huan Liu (*equal contribution)
    AAAI Conference on Artificial Intelligence (AAAI), 2021
  7. Session-based Recommendation with Hypergraph Attention Networks [pdf] [code]
    Jianling Wang, Kaize Ding, Ziwei Zhu, and James Caverlee
    SIAM International Conference on Data Mining (SDM), 2021
  8. GLOW : Global Weighted Self-Attention Network for Web Search [pdf] [code]
    Xuan Shan, Chuanjie Liu, Yiqian Xia, Qi Chen, Yusi Zhang, Kaize Ding, Yaobo Liang, Angen Luo, and Yuxiang Luo
    IEEE International Conference on Big Data (BigData), 2021
  9. FBAdTracker: An Interactive Data Collection and Analysis Tool for Facebook Advertisements (demo paper) [pdf] [code]
    Ujun Jeong, Kaize Ding, and Huan Liu
    International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), 2021
2020
  1. Be More with Less: Hypergraph Attention Networks for Inductive Text Classification [pdf] [code]
    Kaize Ding, Jianling Wang, Jundong Li, Dingchneg Li, and Huan Liu
    Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
  2. Graph Prototypical Networks for Few-shot Learning on Attributed Networks [pdf] [code]
    Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, and Huan Liu
    ACM International Conference on Information and Knowledge Management (CIKM), 2020
  3. Inductive Anomaly Detection on Attributed Networks [pdf] [code]
    Kaize Ding, Jundong Li, Nitin Aagarwal, and Huan Liu
    International Joint Conference on Artificial Intelligence (IJCAI), 2020
  4. Key Opinion Leaders in Recommendation Systems: Opinion Elicitation and Diffusion [pdf] [code]
    Jianling Wang*, Kaize Ding*, Ziwei Zhu, Yin Zhang, and James Caverlee (*equal contribution)
    ACM International Conference on Web Search and Data Mining (WSDM), 2020
  5. Next-item Recommendation with Sequential Hypergraphs [pdf] [code]
    Jianling Wang, Kaize Ding, Liangjie Hong, Huan Liu and James Caverlee
    ACM International SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020
  6. Graph Few-shot Learning with Attribute Matching [pdf] [code]
    Ning Wang, Minnan Luo, Kaize Ding, Lingling Zhang, Jundong Li, and Qinghua Zheng
    ACM International Conference on Information and Knowledge Management (CIKM), 2020
  7. Combating Disinformation in a Social Media Age [pdf] [code]
    Kai Shu, Amrita Bhattacharjee, Faisal Alatawi, Tahora H. Nazer, Kaize Ding, Mansooreh Karami, and Huan Liu
    WIREs Data Mining and Knowledge Discovery, 2020
  8. Challenges in Combating COVID-19 Infodemic -- Data, Tools, and Ethics [pdf] [code]
    Kaize Ding, Kai Shu, Yichuan Li, Amrita Bhattacharjee, and Huan Liu
    CIKM 2020 Workshop on Mining Actionable Insights from Social Networks (MAISoN), 2020
2019
  1. InterSpot: Interactive Spammer Detection in Social Media (demo paper) [pdf] [code]
    Kaize Ding, Jundong Li, Shivam Dhar, Shreyash Devan, and Huan Liu
    International Joint Conference on Artificial Intelligence (IJCAI), 2019
  2. Deep Anomaly Detection on Attributed Networks with Graph Convolutional Networks [pdf] [code]
    Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu
    SIAM International Conference on Data Mining (SDM), 2019, Most cited paper since 2018
  3. Interactive Anomaly Detection on Attributed Networks [pdf] [code]
    Kaize Ding, Jundong Li, and Huan Liu
    ACM International Conference on Web Search and Data Mining (WSDM), 2019
Website Templates

created with

Website Builder Software .