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.
PhD in Artificial Intelligence, 2013-2018
Institution of Automation, Chinese Academy of Sciences (CASIA)
BSc in Electronic Science, 2009-2013
Yanshan University
    Selected Publications:
Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks
Junfei Wu, Weizhi Xu, Qiang Liu (corresponding author), Shu Wu and Liang Wang
IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), accepted
Stage-Aware Hierarchical Attentive Relational Network for Diagnosis Prediction
Liping Wang, Qiang Liu, Mengqi Zhang, Yaxuan Hu, Shu Wu and Liang Wang
IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), accepted
DyGCN: Efficient Dynamic Graph Embedding with Graph Convolutional Network
Zeyu Cui, Zekun Li, Shu Wu, Xiaoyu Zhang, Qiang Liu, Liang Wang and Mengmeng Ai
IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), accepted
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
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
Interpretable Multimodal Out-of-context Detection with Soft Logic Regularization
Huanhuan Ma, Jinghao Zhang, Qiang Liu, Shu Wu and Liang Wang
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024
Rethinking Graph Masked Autoencoders through Alignment and Uniformity
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
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
Haisong Gong, Weizhi Xu, Shu Wu, Qiang Liu and Liang Wang
AAAI Conference on Artificial Intelligence (AAAI), 2024
Molecular Contrastive Pretraining with Collaborative Featurizations
Yanqiao Zhu, Dingshuo Chen, Yuanqi Du, Yingze Wang, Qiang Liu and Shu Wu
Journal of Chemical Information and Modeling (JCIM), 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
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design [paper] [arXiv] [code]
Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang and Quanquan Gu
International Conference on Machine Learning (ICML), 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
Attention-based Convolutional Approach for Misinformation Identification from Massive and Noisy Microblog Posts [paper]
Feng Yu, Qiang Liu, Shu Wu, Liang Wang and Tieniu Tan
Computers & Security (COSE), 2019
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
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