I’m an Associate Professor at Institute of Automation, Chinese Academy of Sciences (CASIA). Currently, I mainly focus on data mining, recommender systems, text mining, knowledge graph and graph representation learning. Before that, I received my PhD degree from CASIA, where I mainly focused on recommender systems and text 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 (# Corresponding Author):
Latent Structure Mining With Contrastive Modality Fusion for Multimedia Recommendation [paper] [arXiv] [code]
Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Mengqi Zhang, Shu Wu and Liang Wang
IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), Accepted
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), Accepted
Dynamic Graph Neural Networks for Sequential Recommendation [paper] [code]
Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu and Liang Wang
IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), Accepted
DyGCN: Efficient Dynamic Graph Embedding with Graph Convolutional Network [paper] [arXiv] [code]
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
MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning [paper]
Yuwei Xia, Mengqi Zhang, Qiang Liu#, 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]
Yingtao Luo, Zhaocheng Liu and Qiang Liu#
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
Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention [paper] [code]
Junfei Wu, Qiang Liu, Weizhi Xu and Shu Wu
International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), 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#, Shu Wu, Shuhui Wang and Liang Wang
ACM International Conference on Multimedia (ACMMM), 2021
STAN: Spatio-Temporal Attention Network for Next Point-of-Interest Recommendation [paper] [arXiv] [code]
Yingtao Luo, Qiang Liu# 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
MV-RNN: A Multi-view Recurrent Neural Network for Sequential Recommendation [paper] [arXiv] [code]
Qiang Cui, Shu Wu, Qiang Liu, Wen Zhong and Liang Wang
IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2020
Towards Accurate and Interpretable Sequential Prediction: A CNN & Attention-based Feature Extractor [paper]
Jingyi Wang, Qiang Liu, Zhaocheng Liu and Shu Wu
ACM International Conference on Information and Knowledge Management (CIKM), 2019
Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach [paper] [arXiv]
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] [arXiv]
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] [arXiv]
Qiang Liu, Shu Wu and Liang Wang
IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2017
Context-aware Sequential Recommendation [paper] [arXiv]
Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li and Liang Wang
IEEE International Conference on Data Mining (ICDM), 2016
A Dynamic Recurrent Model for Next Basket Recommendation [paper]
Feng Yu, Qiang Liu, Shu Wu, Liang Wang and Tieniu Tan
International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), 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
COT: Contextual Operating Tensor for Context-aware Recommender Systems [paper]
Qiang Liu, Shu Wu and Liang Wang
AAAI Conference on Artificial Intelligence (AAAI), 2015