• Adaptive Label Smoothing To Regularize Large-Scale Graph Training
    Kaixiong Zhou, Ninghao Liu, Fan Yang, Zirui Liu, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu
    SIAM International Conference on Data Mining (SDM), 2023

  • Interpreting Unfairness in Graph Neural Networks via Training Node Attribution
    Yushun Dong, Song Wang, Jing Ma, Ninghao Liu, Jundong Li
    AAAI Conference on Artificial Intelligence (AAAI), 2023

  • SEAT: Stable and Explainable Attention
    Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang
    AAAI Conference on Artificial Intelligence (AAAI), 2023

  • S2GAE: Self-Supervised Graph Autoencoders Are Generalizable Learners with Graph Masking
    Qiaoyu Tan, Ninghao Liu, Xiao Huang, Soo-Hyun Choi, Li Li, Rui Chen, Xia Hu
    WSDM, 2023

  • Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
    Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu
    WSDM, 2023


  • AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training
    Yili Wang, Kaixiong Zhou, Rui Miao, Ninghao Liu, Xin Wang
    International Conference on Information and Knowledge Management (CIKM), 2022

  • Tutorial on Deep Learning Interpretation: A Data Perspective
    Zhou Yang, Ninghao Liu, Xia Ben Hu, Fang Jin
    International Conference on Information & Knowledge Management (CIKM), 2022

  • GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks
    Weihao Song, Yushun Dong, Ninghao Liu, Jundong Li
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022

  • G-Mixup: Graph Data Augmentation for Graph Classification
    Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu
    International Conference on Machine Learning (ICML), 2022, [Code]
    Outstanding Paper Award

  • DEGREE: Decomposition Based Explanation for Graph Neural Networks
    Qizhang Feng, Ninghao Liu, Fan Yang, Ruixiang Tang, Mengnan Du, Xia Hu
    ICLR, 2022, [Code]

  • EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
    Yushun Dong, Ninghao Liu, Brian Jalaian, Jundong Li
    The Web Conference (WWW), 2022

  • Geometric Graph Representation Learning via Maximizing Rate Reduction
    Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Qingquan Song, Jundong Li, Xia Hu
    The Web Conference (WWW), 2022, [Code]

  • Unseen Anomaly Detection on Networks via Multi-Hpersphere Learning
    Shuang Zhou, Xiao Huang, Ninghao Liu, Qiaoyu Tan, Fu-lai Chung
    SDM, 2022



  • Spam detection in online social media: A survey
    Ninghao Liu, Xia Hu
    Social Media Analytics: Advances and Applications (Jiliang Tang and Charu Aggaral, editors), 2020 (forthcoming with CRC Press).



2017 and ealier

  • Cortical communication via randomized dimensionality reduction with local synaptic connections (poster)
    Christopher Rozell and Ninghao Liu
    Computational and Systems Neuroscience (COSYNE), 2016

  • Spam Detection on Social Networks
    Ninghao Liu, Xia Hu
    Encyclopedia of Social Network Analysis and Mining, 2017