Publications

2024

  • From language modeling to instruction following: Understanding the behavior shift in LLMs after instruction tuning
    Xuansheng Wu, Wenlin Yao, Jianshu Chen, Xiaoman Pan, Xiaoyang Wang, Ninghao Liu, Dong Yu
    The North American Chapter of the Association for Computational Linguistics (NAACL), 2024

  • Molecular Data Programming: Towards Molecule Pseudo-labeling with Systematic Weak Supervision
    Xin Juan, Kaixiong Zhou, Ninghao Liu, Tianlong Chen, Xin Wang
    Conference on Computer Vision and Pattern Recognition (CVPR), 2024

  • Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendation
    Xuansheng Wu, Huachi Zhou, Yucheng Shi, Wenlin Yao, Xiao Huang, Ninghao Liu
    The Web Conference (WWW), 2024

  • Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models
    Yili Wang, Kaixiong Zhou, Ninghao Liu, Ying Wang, Xin Wang
    ICLR, 2024

  • Explainability for large language models: A survey
    Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Mengnan Du
    ACM Transactions on Intelligent Systems and Technology (TIST), 2024

  • BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks
    Zihan Guan, Mengxuan Hu, Zhongliang Zhou, Jielu Zhang, Sheng Li, Ninghao Liu
    AAAI, 2024 (student abstract)

  • Automated Natural Language Explanation of Deep Visual Neurons with Large Models
    Chenxu Zhao, Wei Qian, Yucheng Shi, Mengdi Huai, Ninghao Liu
    AAAI, 2024 (student abstract)

2023

  • Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
    Wenxiong Liao et al.
    IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2023

  • Black-box Backdoor Defense via Zero-shot Image Purification
    Yucheng Shi, Mengnan Du, Xuansheng Wu, Zihan Guan, Jin Sun, Ninghao Liu
    Conference on Neural Information Processing Systems (NeurIPS), 2023
    [code]

  • Double Wins: Boosting Accuracy and Efficiency of Graph Neural Networks by Reliable Knowledge Distillation
    Qiaoyu Tan, Daochen Zha, Ninghao Liu, Soo-Hyun Choi, Li Li, Rui Chen and Xia Hu
    IEEE International Conference on Data Mining (ICDM), 2023

  • GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction
    Yucheng Shi, Yushun Dong, Qiaoyu Tan, Jundong Li, Ninghao Liu
    The Conference on Information and Knowledge Management (CIKM), 2023
    [code]

  • Attacking Neural Networks with Neural Networks: Towards Deep Synchronization for Backdoor Attacks
    Zihan Guan, Lichao Sun, Mengnan Du, Ninghao Liu
    The Conference on Information and Knowledge Management (CIKM), 2023
    [code]

  • XGBD: Explanation-Guided Graph Backdoor Detection
    Zihan Guan, Mengnan Du, Ninghao Liu
    European Conference on Artificial Intelligence (ECAI), 2023
    [code]

  • ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning
    Yucheng Shi, Kaixiong Zhou, Ninghao Liu
    ECML-PKDD, 2023
    [code]

  • Mitigating Algorithmic Bias with Limited Annotations
    Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou, Xia Hu
    ECML-PKDD, 2023

  • DIVISION: Memory Efficient Training via Dual Activation Precision
    Guanchu Wang, Zirui Liu, Zhimeng Jiang, Ninghao Liu, Na Zou, Xia Hu
    International Conference on Machine Learning (ICML), 2023

  • Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation
    Shuang Zhou, Xiao Huang, Ninghao Liu, Fu-Lai Chung, Long-Kai Huang
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023

  • Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Education
    Xuansheng Wu, Xinyu He, Tianming Liu, Ninghao Liu, Xiaoming Zhai
    International Conference on Artifical Intelligence in Education (AIED), 2023
    [code]

  • Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking
    Ruixiang Tang, Qizhang Feng, Ninghao Liu, Fan Yang, Xia Hu
    SIGKDD Exploration Newsletter, 2023

  • 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
    International Conference on Web Search and Data Mining (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
    International Conference on Web Search and Data Mining (WSDM), 2023

2022

  • 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
    [slides]

  • 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

2021

2020

  • 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).

2019

2018

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