Qiang Liu

Qiang Liu

Associate Professor

Chinese Academy of Sciences

Biography

Qiang Liu is an associate professor at Institute of Automation, Chinese Academy of Sciences (CASIA). Currently, he mainly focuses on LLM safety, misinformation detection and AI for science. Before that, he received his PhD degree from CASIA, where he mainly focused on data mining.

Interests

  • Data Mining
  • LLM Safety
  • Misinformation Detection
  • AI for Science

Education

  • PhD in Artificial Intelligence, 2013-2018

    Institution of Automation, Chinese Academy of Sciences (CASIA)

  • BSc in Electronic Science, 2009-2013

    Yanshan University

Experience

 
 
 
 
 

Associate Professor

Institute of Automation, Chinese Academy of Sciences (CASIA)

Jul 2022 – Present Beijing, China
 
 
 
 
 

Assistant Professor

Institute of Automation, Chinese Academy of Sciences (CASIA)

Mar 2021 – Jun 2022 Beijing, China
 
 
 
 
 

Postdoctoral Researcher

Tsinghua University

Nov 2018 – Mar 2021 Beijing, China
 
 
 
 
 

R&D Director

RealAI

Jul 2018 – Sep 2020 Beijing, China

Publications


    Selected Publications:


  • Out-of-distribution Evidence-aware Fake News Detection via Dual Adversarial Debiasing [paper] [arXiv]

    Qiang Liu, Junfei Wu, Shu Wu and Liang Wang

    IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), accepted


  • Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks [paper] [arXiv] [code]

    Junfei Wu, Weizhi Xu, Qiang Liu (corresponding author), Shu Wu and Liang Wang

    IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), accepted


  • DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization

    Xin Sun, Liang Wang, Qiang Liu, Shu Wu, Zilei Wang and Liang Wang

    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024


  • Rethinking Fair Graph Neural Networks from Re-balancing

    Zhixun Li, Yushun Dong, Qiang Liu and Jeffrey Xu Yu

    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024


  • Stealthy Attack on Large Language Model based Recommendation [arXiv]

    Jinghao Zhang, Yuting Liu, Qiang Liu, Shu Wu, Guibing Guo and Liang Wang

    Annual Meeting of the Association for Computational Linguistics (ACL), 2024


  • Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models [arXiv]

    Junfei Wu, Qiang Liu, Ding Wang, Jinghao Zhang, Shu Wu, Liang Wang and Tieniu Tan

    Findings of the Association for Computational Linguistics: ACL, 2024


  • Enhancing Temporal Knowledge Graph Forecasting with Large Language Models via Chain-of-History Reasoning [arXiv]

    Yuwei Xia, Ding Wang, Qiang Liu, Liang Wang, Shu Wu and Xiao-Yu Zhang

    Findings of the Association for Computational Linguistics: ACL, 2024


  • EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification [arXiv]

    Huanhuan Ma, Weizhi Xu, Yifan Wei, Liuji Chen, Liang Wang, Qiang Liu, Shu Wu and Liang Wang

    Findings of the Association for Computational Linguistics: ACL, 2024


  • Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection

    Xiang Tao, Liang Wang, Qiang Liu (corresponding author), Shu Wu and Liang Wang

    The Web Conference (WWW), 2024


  • CAMLO: Cross-Attentive Multi-View Network for Long-Term Origin-Destination Flow Prediction [paper]

    Liang Wang, Hao Fu, Shu Wu, Qiang Liu (corresponding author), Xuelei Tan, Fangsheng Huang, Mengdi Zhang and Wei Wu

    SIAM Conference on Data Mining (SDM), 2024


  • Stage-Aware Hierarchical Attentive Relational Network for Diagnosis Prediction [paper]

    Liping Wang, Qiang Liu, Mengqi Zhang, Yaxuan Hu, Shu Wu and Liang Wang

    IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2024


  • Rethinking Graph Masked Autoencoders through Alignment and Uniformity [paper] [arXiv] [code]

    Liang Wang, Xiang Tao, Qiang Liu, Shu Wu and Liang Wang

    AAAI Conference on Artificial Intelligence (AAAI), 2024


  • Text-Guided Molecule Generation with Diffusion Language Model [paper] [arXiv] [code]

    Haisong Gong, Qiang Liu, Shu Wu and Liang Wang

    AAAI Conference on Artificial Intelligence (AAAI), 2024


  • Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables [paper] [arXiv]

    Haisong Gong, Weizhi Xu, Shu Wu, Qiang Liu and Liang Wang

    AAAI Conference on Artificial Intelligence (AAAI), 2024


  • GSLB: The Graph Structure Learning Benchmark [paper] [arXiv] [code]

    Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu (corresponding author), Shu Wu, Liang Wang and Jeffrey Xu Yu

    Advances in Neural Information Processing Systems (NeurIPS), 2023


  • Uncovering Neural Scaling Law in Molecular Representation Learning [paper] [arXiv] [code]

    Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, Zhixun Li, Qiang Liu, Shu Wu and Liang Wang

    Advances in Neural Information Processing Systems (NeurIPS), 2023


  • Noise-Robust Semi-Supervised Learning for Distantly Supervised Relation Extraction [paper]

    Xin Sun, Qiang Liu (corresponding author), Shu Wu, Zilei Wang and Liang Wang

    Findings of the Association for Computational Linguistics: EMNLP, 2023


  • Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations [paper] [arXiv]

    Yingtao Luo, Qiang Liu (co-first and corresponding author), Yuntian Chen, Wenbo Hu, Tian Tian and Jun Zhu

    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023


  • Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation [paper] [arXiv]

    Jinghao Zhang, Qiang Liu, Shu Wu and Liang Wang

    International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), 2023


  • Counterfactual Debiasing for Fact Verification [paper]

    Weizhi Xu, Qiang Liu (co-first author), Shu Wu and Liang Wang

    Annual Meeting of the Association for Computational Linguistics (ACL), 2023


  • Learning Latent Relations for Temporal Knowledge Graph Reasoning [paper]

    Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu and Liang Wang

    Annual Meeting of the Association for Computational Linguistics (ACL), 2023


  • RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring [paper] [arXiv]

    Qiang Liu, Yingtao Luo, Shu Wu, Zhen Zhang, Xiangnan Yue, Hong Jin and Liang Wang

    IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2023


  • Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning [paper] [code]

    Mengqi Zhang, Yuwei Xia, Qiang Liu (corresponding author), Shu Wu and Liang Wang

    The Web Conference (WWW), 2023


  • Dynamic Graph Neural Networks for Sequential Recommendation [paper] [arXiv] [code]

    Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu and Liang Wang

    IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2023


  • MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning [paper] [arXiv]

    Yuwei Xia, Mengqi Zhang, Qiang Liu (corresponding author), Shu Wu and Xiao-Yu Zhang

    Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022


  • Deep Stable Representation Learning on Electronic Health Records [paper] [arXiv] [code]

    Yingtao Luo, Zhaocheng Liu and Qiang Liu (corresponding author)

    IEEE International Conference on Data Mining (ICDM), 2022


  • The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection [paper] [arXiv]

    Zhixun Li, Dingshuo Chen, Qiang Liu and Shu Wu

    IEEE International Conference on Data Mining (ICDM), 2022


  • GraphDIVE: Graph Classification by Mixture of Diverse Experts [paper] [arXiv]

    Fenyu Hu, Liping Wang, Qiang Liu, Shu Wu, Liang Wang and Tieniu Tan

    International Joint Conference on Artificial Intelligence (IJCAI), 2022


  • Evidence-aware Fake News Detection with Graph Neural Networks [paper] [arXiv] [code]

    Weizhi Xu, Junfei Wu, Qiang Liu, Shu Wu and Liang Wang

    The Web Conference (WWW), 2022


  • An Empirical Study of Graph Contrastive Learning [paper] [arXiv] [code]

    Yanqiao Zhu, Yichen Xu, Qiang Liu and Shu Wu

    Advances in Neural Information Processing Systems (NeurIPS), 2021


  • Mining Cross Features for Financial Credit Risk Assessment [paper] [arXiv]

    Qiang Liu, Zhaocheng Liu, Haoli Zhang, Yuntian Chen and Jun Zhu

    ACM International Conference on Information and Knowledge Management (CIKM), 2021


  • Mining Latent Structures for Multimedia Recommendation [paper] [arXiv] [code]

    Jinghao Zhang, Yanqiao Zhu, Qiang Liu (corresponding author), Shu Wu, Shuhui Wang and Liang Wang

    ACM International Conference on Multimedia (ACMMM), 2021


  • STAN: Spatio-Temporal Attention Network for Next Location Recommendation [paper] [arXiv] [code]

    Yingtao Luo, Qiang Liu (corresponding author) and Zhaocheng Liu

    The Web Conference (WWW), 2021


  • Graph Contrastive Learning with Adaptive Augmentation [paper] [arXiv] [code]

    Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu and Liang Wang

    The Web Conference (WWW), 2021


  • Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach [paper]

    Qiang Liu, Feng Yu, Shu Wu and Liang Wang

    ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2018


  • A Convolutional Approach for Misinformation Identification [paper]

    Feng Yu, Qiang Liu, Shu Wu, Liang Wang and Tieniu Tan

    International Joint Conference on Artificial Intelligence (IJCAI), 2017


  • DeepStyle: Learning User Preferences for Visual Recommendation [paper]

    Qiang Liu, Shu Wu and Liang Wang

    International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), 2017


  • Multi-behavioral Sequential Prediction with Recurrent Log-bilinear Model [paper]

    Qiang Liu, Shu Wu and Liang Wang

    IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2017


  • Context-aware Sequential Recommendation [paper]

    Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li and Liang Wang

    IEEE International Conference on Data Mining (ICDM), 2016


  • Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts [paper]

    Qiang Liu, Shu Wu, Liang Wang and Tieniu Tan

    AAAI Conference on Artificial Intelligence (AAAI), 2016


  • A Dynamic Recurrent Model for Next Basket Recommendation [paper]

    Feng Yu, Qiang Liu (co-first author), Shu Wu, Liang Wang and Tieniu Tan

    International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), 2016


  • Contextual Operation for Recommender Systems [paper]

    Shu Wu, Qiang Liu (co-first author), Liang Wang and Tieniu Tan

    IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2016


  • Collaborative Prediction for Multi-entity Interaction with Hierarchical Representation [paper]

    Qiang Liu, Shu Wu and Liang Wang

    ACM International Conference on Information and Knowledge Management (CIKM), 2015


  • COT: Contextual Operating Tensor for Context-aware Recommender Systems [paper]

    Qiang Liu, Shu Wu and Liang Wang

    AAAI Conference on Artificial Intelligence (AAAI), 2015