Publications

Representative papers

• Graph Contrastive Learning with Adaptive Augmentation.
  Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang. International World Wide Web Conference, 2021. [bib]
  

  @inproceedings{zhu2021graph,
          title={Graph contrastive learning with adaptive augmentation},
          author={Zhu, Yanqiao and Xu, Yichen and Yu, Feng and Liu, Qiang and Wu, Shu and Wang, Liang},
          booktitle={Proceedings of the Web Conference 2021},
          pages={2069--2080},
          year={2021}
          }
  

• Session-based Recommendation with Graph Neural Network.
  Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan. The Association for the Advancement of Artificial Intelligence, 2019. [bib]
  

  @inproceedings{wu2019session,
          title={Session-based recommendation with graph neural networks},
          author={Wu, Shu and Tang, Yuyuan and Zhu, Yanqiao and Wang, Liang and Xie, Xing and Tan, Tieniu},
          booktitle={Proceedings of the AAAI conference on artificial intelligence},
          volume={33},
          number={01},
          pages={346--353},
          year={2019}
          }
  

• A Convolutional Approach for Misinformation Identification.
  Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. International Joint Conference on Artificial Intelligence, 2017. [bib]
  

  @inproceedings{yu2017convolutional,
          title={A Convolutional Approach for Misinformation Identification.},
          author={Yu, Feng and Liu, Qiang and Wu, Shu and Wang, Liang and Tan, Tieniu and others},
          booktitle={IJCAI},
          pages={3901--3907},
          year={2017}
          }
  

• Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts.
  Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. The Association for the Advancement of Artificial Intelligence, 2016. [bib]
  

  @inproceedings{liu2016predicting,
          title={Predicting the next location: A recurrent model with spatial and temporal contexts},
          author={Liu, Qiang and Wu, Shu and Wang, Liang and Tan, Tieniu},
          booktitle={Proceedings of the AAAI conference on artificial intelligence},
          volume={30},
          number={1},
          year={2016}
          }
  

2024

• VLKEB: A Large Vision-Language Model Knowledge Editing Benchmark, Datasets and Benchmarks Track.
  Han Huang, Haitian Zhong, Tao Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. Conference and Workshop on Neural Information Processing Systems (NeurIPS), 2024. [bib]
• Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization.
  Dingshuo Chen, Zhixun Li, Yuyan Ni, Guibin Zhang, Ding Wang, Qiang Liu, Shu Wu, Jeffrey Xu Yu, Liang Wang. Conference and Workshop on Neural Information Processing Systems (NeurIPS), 2024. [bib]

  @article{chen2024beyond,
    title={Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization},
    author={Chen, Dingshuo and Li, Zhixun and Ni, Yuyan and Zhang, Guibin and Wang, Ding and Liu, Qiang and Wu, Shu and Yu, Jeffrey Xu and Wang, Liang},
    journal={arXiv preprint arXiv:2409.01081},
    year={2024}
  }
  

• Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-shot Molecular Property Prediction.
  Liang Wang, Qiang Liu, Shaozhen Liu, Xin Sun, Shu Wu, Liang Wang. Conference and Workshop on Neural Information Processing Systems (NeurIPS), 2024. [bib]
• Knowledge Graph Enhanced Large Language Model Editing.
  Mengqi zhang, Xiaotian Ye, Qiang Liu, PengjieRen, Shu Wu, Zhumin Chen. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024. [bib]

  @article{zhang2024knowledge,
    title={Knowledge graph enhanced large language model editing},
    author={Zhang, Mengqi and Ye, Xiaotian and Liu, Qiang and Ren, Pengjie and Wu, Shu and Chen, Zhumin},
    journal={arXiv preprint arXiv:2402.13593},
    year={2024}
  }
  

• Modality-Balanced Learning for Multimedia Recommendation.
  Jinghao Zhang, Guofan Liu, Qiang Liu, Shu Wu, Liang Wang. ACM International Conference on Multimedia(ACM MM), 2024. [bib]
  

  Zhang J, Liu G, Liu Q, et al. Modality-Balanced Learning for Multimedia Recommendation[C]//ACM Multimedia 2024.
  

• Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media.
  Jiajun Zhang, Zhixun Li, Qiang Liu, Shu Wu, Liang Wang. ACM International Conference on Information and Knowledge Management (CIKM), 2024. [bib]
  

  还没有bib
  

• DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generaliztion.
  Xin Sun, Liang Wang, Qiang Liu, Shu Wu, Zilei Wang, Liang Wang.Knowledge Discovery and Data Mining, 2024. [bib]
  

  还没有bib
  

• Stealthy Attack on Large Language Model based Recommendation.
  Jinghao Zhang, Yuting Liu, Qiang Liu, Shu Wu, Guibing Guo, Liang Wang. Annual Meeting of the Association for Computational Linguistics, 2024. [bib]
  

  @article{zhang2024stealthy,
          title={Stealthy Attack on Large Language Model based Recommendation},
          author={Zhang, Jinghao and Liu, Yuting and Liu, Qiang and Wu, Shu and Guo, Guibing and Wang, Liang},
          journal={arXiv preprint arXiv:2402.14836},
          year={2024}
          }
  

• Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models.
  Junfei Wu, Qiang Liu, Ding Wang, Jinghao Zhang, Shu Wu, Liang Wang, Tieniu Tan. Annual Meeting of the Association for Computational Linguistics, findings, 2024. [bib] [code]

  @article{wu2024logical,
          title={Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models},
          author={Wu, Junfei and Liu, Qiang and Wang, Ding and Zhang, Jinghao and Wu, Shu and Wang, Liang and Tan, Tieniu},
          journal={arXiv preprint arXiv:2402.11622},
          year={2024}
          }
  

• Enhancing Temporal Knowledge Graph Forecasting with Large Language Models via Chain-of-History Reasoning.
  Yuwei Xia, Ding Wang, Qiang Liu, Liang Wang, Shu Wu, Xiao-Yu Zhang. Annual Meeting of the Association for Computational Linguistics, findings, 2024. [bib]
  

  @article{xia2024enhancing,
          title={Enhancing Temporal Knowledge Graph Forecasting with Large Language Models via Chain-of-History Reasoning},
          author={Xia, Yuwei and Wang, Ding and Liu, Qiang and Wang, Liang and Wu, Shu and Zhang, Xiaoyu},
          journal={arXiv preprint arXiv:2402.14382},
          year={2024}
          }
  

• EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification.
  Huanhuan Ma, Weizhi Xu, Yifan Wei, Liuji Chen, Liang Wang, Qiang Liu, Shu Wu, Liang Wang. Annual Meeting of the Association for Computational Linguistics, findings, 2024. [bib]
  

  @article{ma2023ex,
          title={EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification},
          author={Ma, Huanhuan and Xu, Weizhi and Wei, Yifan and Chen, Liuji and Wang, Liang and Liu, Qiang and Wu, Shu},
          journal={arXiv preprint arXiv:2310.09754},
          year={2023}
          }
  

• MetaTKG++: Learning Evolving Factor Enhanced Meta-knowledge for Temporal Knowledge Graph Reasoning.
  Yuwei Xia, Mengqi Zhang, Qiang Liu, Liang Wang, Shu Wu, Xiaoyu Zhang, Liang Wang. Pattern Recognition, 2024. [bib]
  

  @article{xia2024metatkg++,
          title={MetaTKG++: Learning evolving factor enhanced meta-knowledge for temporal knowledge graph reasoning},
          author={Xia, Yuwei and Zhang, Mengqi and Liu, Qiang and Wang, Liang and Wu, Shu and Zhang, Xiaoyu},
          journal={Pattern Recognition},
          pages={110629},
          year={2024},
          publisher={Elsevier}
          }
  

• Out-of-distribution Evidence-aware Fake News Detection via Dual Adversarial Debiasing.
  Qiang Liu, Junfei Wu, Shu Wu, Liang Wang. IEEE Transactions on Knowledge and Data Engineering, 2024. [bib]
  

  @article{liu2024out,
          title={Out-of-distribution Evidence-aware Fake News Detection via Dual Adversarial Debiasing},
          author={Liu, Qiang and Wu, Junfei and Wu, Shu and Wang, Liang},
          journal={IEEE Transactions on Knowledge and Data Engineering},
          year={2024},
          publisher={IEEE}
          }
  

• GraphFM: Graph Factorization Machines for Feature Interaction Modeling.
  Shu Wu, Zekun Li, Yunyue Su, Zeyu Cui, Xiaoyu Zhang and Liang Wang. Machine Intelligence Research, 2024. [bib] [code]
  

  @misc{wu2024graphfm,
          title={GraphFM: Graph Factorization Machines for Feature Interaction Modeling},
          author={Shu Wu and Zekun Li and Yunyue Su and Zeyu Cui and Xiaoyu Zhang and Liang Wang},
          year={2024},
          eprint={2105.11866},
          archivePrefix={arXiv},
          primaryClass={cs.LG}
          }
  

• Molecular Contrastive Pretraining with Collaborative Featurizations.
  Yanqiao Zhu, Dingshuo Chen, Yuanqi Du, Yingze Wang, Qiang Liu, Shu Wu. Journal of Chemical Information and Modeling, 2024. [bib]
  

  @article{zhu2024molecular,
          title={Molecular Contrastive Pretraining with Collaborative Featurizations},
          author={Zhu, Yanqiao and Chen, Dingshuo and Du, Yuanqi and Wang, Yingze and Liu, Qiang and Wu, Shu},
          journal={Journal of Chemical Information and Modeling},
          year={2024},
          publisher={ACS Publications}
          }
  

• Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection.
  Xiang Tao, Liang Wang, Qiang Liu, Shu Wu, Liang Wang. International World Wide Web Conference, 2024. [bib]
  

  @inproceedings{10.1145/3589334.3645478,
          author = {Tao, Xiang and Wang, Liang and Liu, Qiang and Wu, Shu and Wang, Liang},
          title = {Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection},
          year = {2024},
          isbn = {9798400701719},
          publisher = {Association for Computing Machinery},
          address = {New York, NY, USA},
          url = {https://doi.org/10.1145/3589334.3645478},
          doi = {10.1145/3589334.3645478},
          abstract = {Due to the rapid spread of rumors on social media, rumor detection has become an extremely important challenge. Recently, numerous rumor detection models which utilize textual information and the propagation structure of events have been proposed. However, these methods overlook the importance of semantic evolvement information of event in propagation process, which is often challenging to be truly learned in supervised training paradigms and traditional rumor detection methods. To address this issue, we propose a novel semantic evolvement enhanced Graph Autoencoder for Rumor Detection (GARD) model in this paper. The model learns semantic evolvement information of events by capturing local semantic changes and global semantic evolvement information through specific graph autoencoder and reconstruction strategies. By combining semantic evolvement information and propagation structure information, the model achieves a comprehensive understanding of event propagation and perform accurate and robust detection, while also detecting rumors earlier by capturing semantic evolvement information in the early stages. Moreover, in order to enhance the model's ability to learn the distinct patterns of rumors and non-rumors, we introduce a uniformity regularizer to further improve the model's performance. Experimental results on three public benchmark datasets confirm the superiority of our GARD method over the state-of-the-art approaches in both overall performance and early rumor detection.},
          booktitle = {Proceedings of the ACM on Web Conference 2024},
          pages = {4150–4159},
          numpages = {10},
          keywords = {graph autoencoder, rumor detection, social media},
          location = {, Singapore, Singapore, },
          series = {WWW '24}
          }
  

• CAMLO: Cross-Attentive Multi-View Network for Long-Term Origin-Destination Flow Prediction.
  Liang Wang, Hao Fu, Shu Wu, Qiang Liu, Xuelei Tan, Fangsheng Huang, Mengdi Zhang, Wei Wu. SIAM International Conference on Data Mining, 2024. [bib]
  

  @inbook{doi:10.1137/1.9781611978032.53,
          author = {Liang Wang and Hao Fu and Shu Wu and Qiang Liu and Xuelei Tan and Fangsheng Huang and Mengdi Zhang and Wei Wu},
          title = {CAMLO: Cross-Attentive Multi-View Network for Long-Term Origin-Destination Flow Prediction},
          booktitle = {Proceedings of the 2024 SIAM International Conference on Data Mining (SDM)},
          chapter = {},
          pages = {454-462},
          doi = {10.1137/1.9781611978032.53},
          URL = {https://epubs.siam.org/doi/abs/10.1137/1.9781611978032.53},
          eprint = {https://epubs.siam.org/doi/pdf/10.1137/1.9781611978032.53},
                  abstract = { Predicting the volume of flow from an origin to a destination is essential to understanding the mobility pattern and improving many transportation services, such as ride-hailing and food delivery. However, making long-term prediction for all possible pairs of origins and destinations is still a challenging problem. Existing works either suffer from cumulative error and high complexity of iterative computation on long sequence, or neglect the complex spatial correlation obscured by elongated time span. In this paper, we present CAMLO, a cross-attentive multi-view network for this task. Our model adopts a multi-view framework that separately models the asymmetric characteristics of origin and destination in origin- and destination-oriented view. In each view, a relational graph aggregation module captures the sparse and multi-relational correlation among origins and destinations. Subsequently, a Transformer-based forecasting module is applied to discover intricate temporal dynamics. The two views internally interact with each other via a cross-attention mechanism and are later fused for the final prediction. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of our model. }
          }
  

• Interpretable Multimodal Out-of-context Detection with Soft Logic Regularization.
  Huanhuan Ma, Jinghao Zhang, Qiang Liu, Shu Wu, Liang Wang. International Conference on Acoustics, Speech, & Signal Processing, 2024. [bib]
  

  @inproceedings{ma2024interpretable,
          title={Interpretable Multimodal Out-of-Context Detection with Soft Logic Regularization},
          author={Ma, Huanhuan and Zhang, Jinghao and Liu, Qiang and Wu, Shu and Wang, Liang},
          booktitle={ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
          pages={4740--4744},
          year={2024},
          organization={IEEE}
          }
  

• Text-Guided Molecule Generation with Diffusion Language Model.
  Haisong Gong, Qiang Liu, Shu Wu, Liang Wang. The Association for the Advancement of Artificial Intelligence, 2024. [bib]
  

  @article{gong2024text,
          title={Text-Guided Molecule Generation with Diffusion Language Model},
          author={Gong, Haisong and Liu, Qiang and Wu, Shu and Wang, Liang},
          journal={arXiv preprint arXiv:2402.13040},
          year={2024}
          }
  

• Rethinking Graph Masked Autoencoders through Alignment and Uniformity.
  Liang Wang, Xiang Tao, Qiang Liu, Shu Wu, Liang Wang. The Association for the Advancement of Artificial Intelligence, 2024. [bib]
  

  @article{wang2024rethinking,
          title={Rethinking Graph Masked Autoencoders through Alignment and Uniformity},
          author={Wang, Liang and Tao, Xiang and Liu, Qiang and Wu, Shu},
          journal={arXiv preprint arXiv:2402.07225},
          year={2024}
          }
  

• Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables.
  Haisong Gong, Weizhi Xu, Shu Wu, Qiang Liu, Liang Wang. The Association for the Advancement of Artificial Intelligence, 2024. [bib]
  

  @inproceedings{gong2024heterogeneous,
          title={Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables},
          author={Gong, Haisong and Xu, Weizhi and Wu, Shu and Liu, Qiang and Wang, Liang},
          booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
          volume={38},
          number={1},
          pages={100--108},
          year={2024}
          }
  

2023

• Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks.
  Junfei Wu, Weizhi Xu, Qiang Liu, Shu Wu, Liang Wang. IEEE Transactions on Knowledge and Data Engineering, 2023. [bib]
  

  @article{wu2023adversarial,
          title={Adversarial contrastive learning for evidence-aware fake news detection with graph neural networks},
          author={Wu, Junfei and Xu, Weizhi and Liu, Qiang and Wu, Shu and Wang, Liang},
          journal={IEEE Transactions on Knowledge and Data Engineering},
          year={2023},
          publisher={IEEE}
          }
  

• Noise-Robust Semi-Supervised Learning for Distantly Supervised Relation Extraction.
  Xin Sun, Qiang Liu, Shu Wu, Liang Wang, Zilei Wang. Conference on Empirical Methods in Natural Language Processing, Findings, 2023. [bib]
  

  @inproceedings{sun2023noise,
          title={Noise-Robust Semi-Supervised Learning for Distantly Supervised Relation Extraction},
          author={Sun, Xin and Liu, Qiang and Wu, Shu and Wang, Zilei and Wang, Liang},
          booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
          pages={13145--13157},
          year={2023}
          }
  

• GSLB: The Graph Structure Learning Benchmark.
  Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, Liang Wang, Jeffrey Xu Yu. Conference and Workshop on Neural Information Processing Systems, 2023. [bib]
  

  @article{li2024gslb,
          title={GSLB: The Graph Structure Learning Benchmark},
          author={Li, Zhixun and Sun, Xin and Luo, Yifan and Zhu, Yanqiao and Chen, Dingshuo and Luo, Yingtao and Zhou, Xiangxin and Liu, Qiang and Wu, Shu and Wang, Liang and others},
          journal={Advances in Neural Information Processing Systems},
          volume={36},
          year={2024}
          }
  

• Uncovering Neural Scaling Law in Molecular Representation Learning.
  Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, Zhixun Li, Qiang Liu, Shu Wu, Liang Wang. Conference and Workshop on Neural Information Processing Systems, 2023. [bib]
  

  @article{chen2024uncovering,
          title={Uncovering neural scaling laws in molecular representation learning},
          author={Chen, Dingshuo and Zhu, Yanqiao and Zhang, Jieyu and Du, Yuanqi and Li, Zhixun and Liu, Qiang and Wu, Shu and Wang, Liang},
          journal={Advances in Neural Information Processing Systems},
          volume={36},
          year={2024}
          }
  

• A Robust Multi-site Brain Network Analysis Framework based on Federated Learning for Brain Disease Diagnosis.
  Chang Zhang, Qiang Liu, Shu Wu, Liang Wang, Huangsheng Ning. Neurocomputing, 2023. [bib]
  

  @article{zhang2023fedbrain,
          title={FedBrain: A robust multi-site brain network analysis framework based on federated learning for brain disease diagnosis},
          author={Zhang, Chang and Meng, Xiangzhu and Liu, Qiang and Wu, Shu and Wang, Liang and Ning, Huansheng},
          journal={Neurocomputing},
          volume={559},
          pages={126791},
          year={2023},
          publisher={Elsevier}
          }
  

• Stage-Aware Hierarchical Attentive Relational Network for Diagnosis Prediction.
  Liping Wang, Qiang Liu, Mengqi Zhang, Yaxuan Hu, Shu Wu, Liang Wang. IEEE Transactions on Knowledge and Data Engineering, 2023. [bib]
  

  @article{wang2023stage,
          title={Stage-aware hierarchical attentive relational network for diagnosis prediction},
          author={Wang, Liping and Liu, Qiang and Zhang, Mengqi and Hu, Yaxuan and Wu, Shu and Wang, Liang},
          journal={IEEE Transactions on Knowledge and Data Engineering},
          year={2023},
          publisher={IEEE}
          }
  

• Personalized Interest Sustainability Modeling for Sequential POI Recommendation.
  Zewen Long, Liang Wang, Qiang Liu, Shu Wu. ACM International Conference on Information and Knowledge Management (CIKM), 2023. [bib]
  

  @inproceedings{long2023personalized,
          title={Personalized Interest Sustainability Modeling for Sequential POI Recommendation},
          author={Long, Zewen and Wang, Liang and Liu, Qiang and Wu, Shu},
          booktitle={Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
          pages={4145--4149},
          year={2023}
          }
  

• Unsupervised Graph Representation Learning with Cluster-Aware Self-Training and Refining.
  Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu. ACM Transactions on Intelligent Systems and Technology (TIST), 2023. [bib]
  

  @article{zhu2023unsupervised,
          title={Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining},
          author={Zhu, Yanqiao and Xu, Yichen and Yu, Feng and Liu, Qiang and Wu, Shu},
          journal={ACM Transactions on Intelligent Systems and Technology},
          volume={14},
          number={5},
          pages={1--21},
          year={2023},
          publisher={ACM New York, NY}
          }
  

• Learning Latent Relations for Temporal Knowledge Graph Reasoning.
  Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, Liang Wang. Annual Meeting of the Association for Computational Linguistics, findings (ACL), 2023. [bib]
  

  @inproceedings{zhang2023learning,
          title={Learning latent relations for temporal knowledge graph reasoning},
          author={Zhang, Mengqi and Xia, Yuwei and Liu, Qiang and Wu, Shu and Wang, Liang},
          booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
          pages={12617--12631},
          year={2023}
          }
  

• Counterfactual Debiasing for Fact Verification.
  Weizhi Xu, Qiang Liu, Shu Wu, Liang Wang. Annual Meeting of the Association for Computational Linguistics, findings (ACL), 2023. [bib]
  

  @inproceedings{xu2023counterfactual,
          title={Counterfactual debiasing for fact verification},
          author={Xu, Weizhi and Liu, Qiang and Wu, Shu and Wang, Liang},
          booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
          pages={6777--6789},
          year={2023}
          }
  

• Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation.
  Jinghao Zhang, Qiang Liu, Shu Wu, Liang Wang. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023. [bib]
  

  @inproceedings{zhang2023mining,
          title={Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation},
          author={Zhang, Jinghao and Liu, Qiang and Wu, Shu and Wang, Liang},
          booktitle={Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
          pages={443--452},
          year={2023}
          }
  

• Improving Multi-Task GNNs for Molecular Property Prediction via Missing Label Imputation.
  Fenyu Hu, Dingshuo Chen, Qiang Liu, Shu Wu, Liang Wang. Machine Intelligence Research (MIR), 2023. [bib]
  

  bib
  

• Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning.
  Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, Liang Wang. International World Wide Web Conference (WWW), 2023. [bib]
  

  @inproceedings{zhang2023learning,
          title={Learning long-and short-term representations for temporal knowledge graph reasoning},
          author={Zhang, Mengqi and Xia, Yuwei and Liu, Qiang and Wu, Shu and Wang, Liang},
          booktitle={Proceedings of the ACM Web Conference 2023},
          pages={2412--2422},
          year={2023}
          }
  

• Explainable Enterprise Credit Rating using Deep Feature Crossing.
  Weiyu Guo, Zhijiang Yang, Shu Wu, Fu Chen, Xiuli Wang. Expert Systems With Applications, 2023. [bib]
  

  @article{guo2023explainable,
          title={Explainable enterprise credit rating using deep feature crossing},
          author={Guo, Weiyu and Yang, Zhijiang and Wu, Shu and Wang, Xiuli and Chen, Fu},
          journal={Expert Systems with Applications},
          volume={220},
          pages={119704},
          year={2023},
          publisher={Elsevier}
          }
  

2022

• AI in Human-computer Gaming: Techniques, Challenges and Opportunities.
  Qiyue Yin, Jun Yang, Kaiqi Huang, Meijing Zhao, Wancheng Ni, Bin Liang, Yan Huang, Shu Wu, Liang Wang. Machine Intelligence Research (MIR), 2022. [bib]
  

  @article{yin2023ai,
          title={Ai in human-computer gaming: Techniques, challenges and opportunities},
          author={Yin, Qi-Yue and Yang, Jun and Huang, Kai-Qi and Zhao, Mei-Jing and Ni, Wan-Cheng and Liang, Bin and Huang, Yan and Wu, Shu and Wang, Liang},
          journal={Machine intelligence research},
          volume={20},
          number={3},
          pages={299--317},
          year={2023},
          publisher={Springer}
          }
  

• A Survey on Deep Graph Generation: Methods and Application.
  Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, Shu Wu. Learning on Graphs Conference (LOG), 2022. [bib]
  

  @inproceedings{zhu2022survey,
          title={A survey on deep graph generation: Methods and applications},
          author={Zhu, Yanqiao and Du, Yuanqi and Wang, Yinkai and Xu, Yichen and Zhang, Jieyu and Liu, Qiang and Wu, Shu},
          booktitle={Learning on Graphs Conference},
          pages={47--1},
          year={2022},
          organization={PMLR}
          }
  

• Second-Order Global Attention Networks for Graph Classification and Regression.
  Fenyu Hu, Zeyu Cui, Shu Wu, Qiang Liu, Jinlin Wu, Liang Wang, Tieniu Tan. International Conference on Artificial Intelligence (CICAI), 2022. [bib]
  

  @inproceedings{hu2022second,
          title={Second-Order Global Attention Networks for Graph Classification and Regression},
          author={Hu, Fenyu and Cui, Zeyu and Wu, Shu and Liu, Qiang and Wu, Jinlin and Wang, Liang and Tan, Tieniu},
          booktitle={CAAI International Conference on Artificial Intelligence},
          pages={496--507},
          year={2022},
          organization={Springer}
          }
  

• Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation.
  jinghao Zhang, Yanqiao Zhu, Qiang Liu, Mengqi Zhang, Shu Wu, Liang Wang. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022. [bib]
  

  @article{zhang2022latent,
          title={Latent structure mining with contrastive modality fusion for multimedia recommendation},
          author={Zhang, Jinghao and Zhu, Yanqiao and Liu, Qiang and Zhang, Mengqi and Wu, Shu and Wang, Liang},
          journal={IEEE Transactions on Knowledge and Data Engineering},
          year={2022},
          publisher={IEEE}
          }
  

• MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning.
  Yuwei Xia, Mengqi Zhang, Qiang Liu, Shu Wu, Xiao Yu Zhang. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022. [bib]
  

  @article{xia2023metatkg,
          title={Metatkg: Learning evolutionary meta-knowledge for temporal knowledge graph reasoning},
          author={Xia, Yuwei and Zhang, Mengqi and Liu, Qiang and Wu, Shu and Zhang, Xiao-Yu},
          journal={arXiv preprint arXiv:2302.00893},
          year={2023}
          }
  

• GraphDIVE: Graph Classification by Mixture of Diverse Experts.
  Fenyu Hu, Liping Wang, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. International Joint Conference on Artificial Intelligence (IJCAI), 2022. [bib]
  

  @inproceedings{hu2022graphdive,
          title={GraphDIVE: Graph Classification by Mixture of Diverse Experts.},
          author={Hu, Fenyu and Wang, Liping and Liu, Qiang and Wu, Shu and Wang, Liang and Tan, Tieniu},
          booktitle={IJCAI},
          pages={2080--2086},
          year={2022}
          }
  

• Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention.
  Junfei Wu, Qiang Liu, Weizhi Xu, Shu Wu. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2022. [bib]
  

  @inproceedings{wu2022bias,
          title={Bias mitigation for evidence-aware fake news detection by causal intervention},
          author={Wu, Junfei and Liu, Qiang and Xu, Weizhi and Wu, Shu},
          booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
          pages={2308--2313},
          year={2022}
          }
  

• Deep Contrastive Multiview Network Embedding.
  Mengqi Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang. ACM International Conference on Information and Knowledge Management (CIKM), 2022. [bib]
  

  @inproceedings{zhang2022deep,
          title={Deep contrastive multiview network embedding},
          author={Zhang, Mengqi and Zhu, Yanqiao and Liu, Qiang and Wu, Shu and Wang, Liang},
          booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
          pages={4692--4696},
          year={2022}
          }
  

• The Devil is in the Conflict: Disentangled Information Graph Neural Networks For Fraud Detection.
  Zhixun Li, Dingshuo Chen, Qiang Liu, Shu Wu. IEEE International Conference on Data Mining (ICDM), 2022. [bib]
  

  @inproceedings{li2022devil,
          title={The devil is in the conflict: Disentangled information graph neural networks for fraud detection},
          author={Li, Zhixun and Chen, Dingshuo and Liu, Qiang and Wu, Shu},
          booktitle={2022 IEEE International Conference on Data Mining (ICDM)},
          pages={1059--1064},
          year={2022},
          organization={IEEE}
          }
  

• A Unified Framework Based on Graph Consensus Term for Multiview Learning.
  Xiangzhu Meng, Lin Feng, Chonghui Guo, Huibing Wang, Shu Wu. IEEE Transactions on Neural Networks and Learning System (TNNLS), 2022. [bib]
  

  @article{meng2022unified,
          title={A unified framework based on graph consensus term for multiview learning},
          author={Meng, Xiangzhu and Feng, Lin and Guo, Chonghui and Wang, Huibing and Wu, Shu},
          journal={IEEE Transactions on Neural Networks and Learning Systems},
          volume={35},
          number={3},
          pages={3964--3977},
          year={2022},
          publisher={IEEE}
          }
  

• RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring.
  Qiang Liu, Yingtao Luo, Shu Wu, Zhen Zhang, Xiangnan Yue, Hong Jin, Liang Wang. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022. [bib]
  

  @article{liu2022rmt,
          title={RMT-Net: Reject-aware multi-task network for modeling missing-not-at-random data in financial credit scoring},
          author={Liu, Qiang and Luo, Yingtao and Wu, Shu and Zhang, Zhen and Yue, Xiangnan and Jin, Hong and Wang, Liang},
          journal={IEEE Transactions on Knowledge and Data Engineering},
          volume={35},
          number={7},
          pages={7427--7439},
          year={2022},
          publisher={IEEE}
          }
  

• DyGCN: Efficient Dynamic Graph Embedding with Graph Convolutional Network.
  Zeyu Cui, Zekun Li, Shu Wu, Xiaoyu Zhang, Qiang Liu, Liang Wang, Mengmeng Ai. IEEE Transactions on Neural Networks and Learning System (TNNLS), 2022. [bib]
  

  @article{cui2022dygcn,
          title={Dygcn: Efficient dynamic graph embedding with graph convolutional network},
          author={Cui, Zeyu and Li, Zekun and Wu, Shu and Zhang, Xiaoyu and Liu, Qiang and Wang, Liang and Ai, Mengmeng},
          journal={IEEE Transactions on Neural Networks and Learning Systems},
          volume={35},
          number={4},
          pages={4635--4646},
          year={2022},
          publisher={IEEE}
          }
  

• Evidence-aware Fake News Detection with Graph Neural Networks.
  Weizhi Xu, Junfei Wu, Qiang Liu, Shu Wu, Liang Wang. International World Wide Web Conference (WWW), 2022. [bib]
  

  @inproceedings{xu2022evidence,
          title={Evidence-aware fake news detection with graph neural networks},
          author={Xu, Weizhi and Wu, Junfei and Liu, Qiang and Wu, Shu and Wang, Liang},
          booktitle={Proceedings of the ACM web conference 2022},
          pages={2501--2510},
          year={2022}
          }
  

• Structure-Enhanced Heterogeneous Graph Contrastive Learning.
  Yanqiao Zhu, Yichen Xu, Hejie Cui, Carl Yang, Qiang Liu, Shu Wu. SIAM International Conference on Data Mining (SDM), 2022. [bib]
  

  @inproceedings{zhu2022structure,
          title={Structure-enhanced heterogeneous graph contrastive learning},
          author={Zhu, Yanqiao and Xu, Yichen and Cui, Hejie and Yang, Carl and Liu, Qiang and Wu, Shu},
          booktitle={Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)},
          pages={82--90},
          year={2022},
          organization={SIAM}
          }
  

• Structure-Enhanced Heterogeneous Graph Contrastive Learning.
  Yanqiao Zhu, Yichen Xu, Hejie Cui, Carl Yang, Qiang Liu, Shu Wu. SIAM International Conference on Data Mining (SDM), 2022. [bib]
  

  @inproceedings{zhu2022structure,
          title={Structure-enhanced heterogeneous graph contrastive learning},
          author={Zhu, Yanqiao and Xu, Yichen and Cui, Hejie and Yang, Carl and Liu, Qiang and Wu, Shu},
          booktitle={Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)},
          pages={82--90},
          year={2022},
          organization={SIAM}
          }
  

• Dynamic Graph Neural Networks for Sequential Recommendation.
  Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, Liang Wang. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022. [bib]

  @article{zhang2022dynamic,
    title={Dynamic graph neural networks for sequential recommendation},
    author={Zhang, Mengqi and Wu, Shu and Yu, Xueli and Liu, Qiang and Wang, Liang},
    journal={IEEE Transactions on Knowledge and Data Engineering},
    volume={35},
    number={5},
    pages={4741--4753},
    year={2022},
    publisher={IEEE}
  }
  

• Personalized graph neural networks with attention mechanism for session-aware recommendation.
  Mengqi Zhang, Shu Wu, Meng Gao, Xin Jiang, Ke Xu, Liang Wang. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022. [bib]

  @article{zhang2020personalized,
    title={Personalized graph neural networks with attention mechanism for session-aware recommendation},
    author={Zhang, Mengqi and Wu, Shu and Gao, Meng and Jiang, Xin and Xu, Ke and Wang, Liang},
    journal={IEEE Transactions on Knowledge and Data Engineering},
    volume={34},
    number={8},
    pages={3946--3957},
    year={2020},
    publisher={IEEE}
  }
  

2021

• An Empirical Study of Graph Contrastive Learning.
  Yanqiao Zhu, Yichen Xu, Qiang Liu, Shu Wu. Conference and Workshop on Neural Information Processing Systems (NeurIPS), 2021. [bib]

  @article{zhu2021empirical,
    title={An empirical study of graph contrastive learning},
    author={Zhu, Yanqiao and Xu, Yichen and Liu, Qiang and Wu, Shu},
    journal={arXiv preprint arXiv:2109.01116},
    year={2021}
  }
  

• A Graph-based Relevance Matching Model for Ad-hoc Retrieval.
  Yufeng Zhang, Jinghao Zhang, Zeyu Cui, Shu Wu, Liang Wang. Proceedings of the AAAI Conference on Artificial Intelligence(AAAI), 2021. [bib]

  @inproceedings{zhang2021graph,
    title={A graph-based relevance matching model for ad-hoc retrieval},
    author={Zhang, Yufeng and Zhang, Jinghao and Cui, Zeyu and Wu, Shu and Wang, Liang},
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    volume={35},
    number={5},
    pages={4688--4696},
    year={2021}
  }
  

• Cold-start Sequential Recommendation via Meta Learner.
  Yujia Zheng, Siyi Liu, Zekun Li, Shu Wu. Proceedings of the AAAI Conference on Artificial Intelligence(AAAI), 2021. [bib]

  @inproceedings{zheng2021cold,
    title={Cold-start sequential recommendation via meta learner},
    author={Zheng, Yujia and Liu, Siyi and Li, Zekun and Wu, Shu},
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    volume={35},
    number={5},
    pages={4706--4713},
    year={2021}
  }
  

• Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval.
  Xueli Yu, Weizhi Xu, Zeyu Cui, Shu Wu, Liang Wang. Proceedings of the Web Conference(WWW), 2021. [bib]

  @inproceedings{yu2021graph,
    title={Graph-based hierarchical relevance matching signals for ad-hoc retrieval},
    author={Yu, Xueli and Xu, Weizhi and Cui, Zeyu and Wu, Shu and Wang, Liang},
    booktitle={Proceedings of the Web Conference 2021},
    pages={778--787},
    year={2021}
  }
  

• Disentangled Item Representation for Recommender Systems.
  Zeyu Cui, Feng Yu, Shu Wu, Qiang Liu, Liang Wang. ACM Transactions on Intelligent Systems and Technology (TIST), 2021. [bib]

  @article{cui2021disentangled,
    title={Disentangled item representation for recommender systems},
    author={Cui, Zeyu and Yu, Feng and Wu, Shu and Liu, Qiang and Wang, Liang},
    journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
    volume={12},
    number={2},
    pages={1--20},
    year={2021},
    publisher={ACM New York, NY, USA}
  }
  

• Mining Latent Structures for Multimedia Recommendation.
  Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Shuhui Wang, Liang Wang. Proceedings of the 29th ACM international conference on multimedia(MM), 2021. [bib]

  @inproceedings{zhang2021mining,
    title={Mining latent structures for multimedia recommendation},
    author={Zhang, Jinghao and Zhu, Yanqiao and Liu, Qiang and Wu, Shu and Wang, Shuhui and Wang, Liang},
    booktitle={Proceedings of the 29th ACM international conference on multimedia},
    pages={3872--3880},
    year={2021}
  }
  

• Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction.
  Yichen Xu, Yanqiao Zhu, Feng Yu, Qiang Liu, Shu Wu. ACM International Conference on Information and Knowledge Management (CIKM), 2021. [bib]

  @inproceedings{xu2021disentangled,
    title={Disentangled self-attentive neural networks for click-through rate prediction},
    author={Xu, Yichen and Zhu, Yanqiao and Yu, Feng and Liu, Qiang and Wu, Shu},
    booktitle={Proceedings of the 30th ACM international conference on information \& knowledge management},
    pages={3553--3557},
    year={2021}
  }
  

• Deep Active Learning for Text Classification with Diverse Interpretations.
  Qiang Liu, Yanqiao Zhu, Zhaocheng Liu, Yufeng Zhang, Shu Wu. ACM International Conference on Information and Knowledge Management (CIKM), 2021. [bib]

  @inproceedings{liu2021deep,
    title={Deep active learning for text classification with diverse interpretations},
    author={Liu, Qiang and Zhu, Yanqiao and Liu, Zhaocheng and Zhang, Yufeng and Wu, Shu},
    booktitle={Proceedings of the 30th ACM international conference on information \& knowledge management},
    pages={3263--3267},
    year={2021}
  }
  

• Label-informed Graph Structure Learning for Node Classification.
  Liping Wang, Fenyu Hu, Shu Wu, Liang Wang. ACM International Conference on Information and Knowledge Management (CIKM), 2021. [bib]

  @inproceedings{wang2021label,
    title={Label-informed graph structure learning for node classification},
    author={Wang, Liping and Hu, Fenyu and Wu, Shu and Wang, Liang},
    booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
    pages={3488--3492},
    year={2021}
  }
  

• Fully Hyperbolic Graph Convolution Network for Recommendation.
  Liping Wang, Fenyu Hu, Shu Wu, Liang Wang. ACM International Conference on Information and Knowledge Management (CIKM), 2021. [bib]

  @inproceedings{wang2021fully,
    title={Fully hyperbolic graph convolution network for recommendation},
    author={Wang, Liping and Hu, Fenyu and Wu, Shu and Wang, Liang},
    booktitle={Proceedings of the 30th ACM international conference on information \& knowledge management},
    pages={3483--3487},
    year={2021}
  }
  

• Motif-aware Sequential Recommendation.
  Zeyu Cui, Yinjiang Cai, Shu Wu, Xibo Ma, Liang Wang. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2021. [bib]

  @inproceedings{cui2021motif,
    title={Motif-aware sequential recommendation},
    author={Cui, Zeyu and Cai, Yinjiang and Wu, Shu and Ma, Xibo and Wang, Liang},
    booktitle={Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
    pages={1738--1742},
    year={2021}
  }
  

• Graph Contrastive Learning with Adaptive Augmentation.
  Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang. International World Wide Web Conference (WWW), 2021. [bib]

  @inproceedings{zhu2021graph,
    title={Graph contrastive learning with adaptive augmentation},
    author={Zhu, Yanqiao and Xu, Yichen and Yu, Feng and Liu, Qiang and Wu, Shu and Wang, Liang},
    booktitle={Proceedings of the web conference 2021},
    pages={2069--2080},
    year={2021}
  }
  

• Relation-aware Heterogeneous Graph for User Profiling.
  Qilong Yan, Yufeng Zhang, Qiang Liu, Shu Wu, Liang Wang. ACM International Conference on Information and Knowledge Management (CIKM), 2021. [bib]

  @inproceedings{yan2021relation,
    title={Relation-aware heterogeneous graph for user profiling},
    author={Yan, Qilong and Zhang, Yufeng and Liu, Qiang and Wu, Shu and Wang, Liang},
    booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
    pages={3573--3577},
    year={2021}
  }
  

2020

• Independence Promoted Graph Disentangled Networks.
  Yanbei Liu, Xiao Wang, Shu Wu, Zhitao Xiao. The Association for the Advancement of Artificial Intelligence (AAAI), 2020. [bib]

  @inproceedings{liu2020independence,
    title={Independence promoted graph disentangled networks},
    author={Liu, Yanbei and Wang, Xiao and Wu, Shu and Xiao, Zhitao},
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    volume={34},
    number={04},
    pages={4916--4923},
    year={2020}
  }
  

• Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks.
  Yufeng Zhang, Xueli Yu, Zeyu Cui, Shu Wu, Zhongzhen Wen, Liang Wang. Annual Meeting of the Association for Computational Linguistics, findings (ACL), 2020. [bib]

  @article{zhang2020every,
    title={Every document owns its structure: Inductive text classification via graph neural networks},
    author={Zhang, Yufeng and Yu, Xueli and Cui, Zeyu and Wu, Shu and Wen, Zhongzhen and Wang, Liang},
    journal={arXiv preprint arXiv:2004.13826},
    year={2020}
  }
  

• TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation.
  Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020. [bib]

  @inproceedings{yu2020tagnn,
    title={TAGNN: Target attentive graph neural networks for session-based recommendation},
    author={Yu, Feng and Zhu, Yanqiao and Liu, Qiang and Wu, Shu and Wang, Liang and Tan, Tieniu},
    booktitle={Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval},
    pages={1921--1924},
    year={2020}
  }
  

• TFNet: Multi-Semantic Feature Interaction for CTR Prediction.
  Shu Wu, Feng Yu, Xueli Yu, Qiang Liu, Liang Wang, Tieniu Tan, Jie Shao, Fan Huang. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020. [bib]

  @inproceedings{wu2020tfnet,
    title={Tfnet: Multi-semantic feature interaction for ctr prediction},
    author={Wu, Shu and Yu, Feng and Yu, Xueli and Liu, Qiang and Wang, Liang and Tan, Tieniu and Shao, Jie and Huang, Fan},
    booktitle={Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval},
    pages={1885--1888},
    year={2020}
  }
  

• Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions.
  Feng Yu, Zhaocheng Liu, Qiang Liu, Haoli Zhang, Shu Wu, Liang Wang. ACM International Conference on Information and Knowledge Management (CIKM), 2020. [bib]

  @inproceedings{yu2020deep,
    title={Deep interaction machine: A simple but effective model for high-order feature interactions},
    author={Yu, Feng and Liu, Zhaocheng and Liu, Qiang and Zhang, Haoli and Wu, Shu and Wang, Liang},
    booktitle={Proceedings of the 29th ACM International Conference on Information \& Knowledge Management},
    pages={2285--2288},
    year={2020}
  }
  

• Deep Graph Contrastive Representation Learning.
  Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang. International Conference on Machine Learning (ICML), 2020. [bib]

  @article{zhu2020deep,
    title={Deep graph contrastive representation learning},
    author={Zhu, Yanqiao and Xu, Yichen and Yu, Feng and Liu, Qiang and Wu, Shu and Wang, Liang},
    journal={arXiv preprint arXiv:2006.04131},
    year={2020}
  }
  

• Dynamic Graph Collaborative Filtering.
  Xiaohan Li, Mengqi Zhang, Shu Wu, Zheng Liu, Liang Wang. International Conference on Data Mining(ICDM), 2020. [bib]

  @inproceedings{li2020dynamic,
    title={Dynamic graph collaborative filtering},
    author={Li, Xiaohan and Zhang, Mengqi and Wu, Shu and Liu, Zheng and Wang, Liang and Philip, S Yu},
    booktitle={2020 IEEE international conference on data mining (ICDM)},
    pages={322--331},
    year={2020},
    organization={IEEE}
  }
  

• GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction.
  Fenyu Hu, Yanqiao Zhu, Shu Wu, Weiran Huang, Liang Wang, Tieniu Tan. Pattern Recognition(PR), 2020. [bib]

  @article{hu2021graphair,
    title={Graphair: Graph representation learning with neighborhood aggregation and interaction},
    author={Hu, Fenyu and Zhu, Yanqiao and Wu, Shu and Huang, Weiran and Wang, Liang and Tan, Tieniu},
    journal={Pattern Recognition},
    volume={112},
    pages={107745},
    year={2021},
    publisher={Elsevier}
  }
  

• MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation.
  Qiang Cui, Shu Wu, Qiang Liu, Wen Zhong, Liang Wang. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020. [bib]

  @article{cui2018mv,
    title={MV-RNN: A multi-view recurrent neural network for sequential recommendation},
    author={Cui, Qiang and Wu, Shu and Liu, Qiang and Zhong, Wen and Wang, Liang},
    journal={IEEE Transactions on Knowledge and Data Engineering},
    volume={32},
    number={2},
    pages={317--331},
    year={2018},
    publisher={IEEE}
  }
  

2019

• Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction.
  Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, Liang Wang. ACM International Conference on Information and Knowledge Management (CIKM), 2019. [bib]

  @inproceedings{li2019fi,
    title={Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction},
    author={Li, Zekun and Cui, Zeyu and Wu, Shu and Zhang, Xiaoyu and Wang, Liang},
    booktitle={Proceedings of the 28th ACM international conference on information and knowledge management},
    pages={539--548},
    year={2019}
  }
  

• Towards Accurate and Interpretable Sequential Prediction: A CNN & Attention-Based Feature Extractor.
  Jingyi Wang, Qiang Liu, Zhaocheng Liu, Shu Wu. ACM International Conference on Information and Knowledge Management (CIKM), 2019. [bib]

  @inproceedings{wang2019towards,
    title={Towards accurate and interpretable sequential prediction: A CNN \& attention-based feature extractor},
    author={Wang, Jingyi and Liu, Qiang and Liu, Zhaocheng and Wu, Shu},
    booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
    pages={1703--1712},
    year={2019}
  }
  

• Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification.
  Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan. International Joint Conference on Artificial Intelligence (IJCAI), 2019. [bib]

  @article{hu2019hierarchical,
    title={Hierarchical graph convolutional networks for semi-supervised node classification},
    author={Hu, Fenyu and Zhu, Yanqiao and Wu, Shu and Wang, Liang and Tan, Tieniu},
    journal={arXiv preprint arXiv:1902.06667},
    year={2019}
  }
  

• A Hierarchical Contextual Attention-based Network for Sequential Recommendation.
  Qiang Cui, Shu Wu, Yan Huang, Liang Wang. Neurocomputing, 2019. [bib]

  @article{cui2019hierarchical,
    title={A hierarchical contextual attention-based network for sequential recommendation},
    author={Cui, Qiang and Wu, Shu and Huang, Yan and Wang, Liang},
    journal={Neurocomputing},
    volume={358},
    pages={141--149},
    year={2019},
    publisher={Elsevier}
  }
  

• Multi-view Clustering via Joint Feature Selection and Partially Constrained Cluster Label Learning.
  Qiyue Yin, Junge Zhang, Shu Wu, Hexi Li. Pattern Recognition(PR), 2019. [bib]

  @article{yin2019multi,
    title={Multi-view clustering via joint feature selection and partially constrained cluster label learning},
    author={Yin, Qiyue and Zhang, Junge and Wu, Shu and Li, Hexi},
    journal={Pattern Recognition},
    volume={93},
    pages={380--391},
    year={2019},
    publisher={Elsevier}
  }
  

• Semi-supervised Compatibility Learning across Categories for Clothing Matching.
  Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, Liang Wang. IEEE International Conference on Multimedia and Expo (ICME), 2019. [bib]

  @inproceedings{li2019semi,
    title={Semi-supervised compatibility learning across categories for clothing matching},
    author={Li, Zekun and Cui, Zeyu and Wu, Shu and Zhang, Xiaoyu and Wang, Liang},
    booktitle={2019 IEEE International Conference on Multimedia and Expo (ICME)},
    pages={484--489},
    year={2019},
    organization={IEEE}
  }
  

• Attention-based Convolutional Approach for Misinformation Identification from Massive and Noisy Microblog Posts.
  Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. computers \& security, 2019. [bib]

  @article{yu2019attention,
    title={Attention-based convolutional approach for misinformation identification from massive and noisy microblog posts},
    author={Yu, Feng and Liu, Qiang and Wu, Shu and Wang, Liang and Tan, Tieniu},
    journal={computers \& security},
    volume={83},
    pages={106--121},
    year={2019},
    publisher={Elsevier}
  }
  

• Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks.
  Zeyu Cui, Zekun Li, Shu Wu, Xiaoyu Zhang, Liang Wang. International World Wide Web Conference (WWW), 2019. [bib]

  @inproceedings{cui2019dressing,
    title={Dressing as a whole: Outfit compatibility learning based on node-wise graph neural networks},
    author={Cui, Zeyu and Li, Zekun and Wu, Shu and Zhang, Xiao-Yu and Wang, Liang},
    booktitle={The world wide web conference},
    pages={307--317},
    year={2019}
  }
  

• Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction.
  Qiang Cui, Yuyuan Tang, Shu Wu, Liang Wang. Pacific Asia Knowledge Discovery and Data Mining(PAKDD), 2019. [bib]

  @inproceedings{cui2019distance2pre,
    title={Distance2pre: Personalized spatial preference for next point-of-interest prediction},
    author={Cui, Qiang and Tang, Yuyuan and Wu, Shu and Wang, Liang},
    booktitle={Advances in Knowledge Discovery and Data Mining: 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part III 23},
    pages={289--301},
    year={2019},
    organization={Springer}
  }
  

2018

• Multi-view Clustering via Unified and View-Specific Embeddings Learning.
  Qiyue Yin, Shu Wu, Liang Wang. IEEE Transactions on Neural Networks and Learning System (TNNLS), 2018. [bib]

  @article{yin2018multiview,
    title={Multiview clustering via unified and view-specific embeddings learning},
    author={Yin, Qiyue and Wu, Shu and Wang, Liang},
    journal={IEEE Transactions on Neural Networks and Learning Systems},
    volume={29},
    number={11},
    pages={5541--5553},
    year={2018},
    publisher={IEEE}
  }
  

• Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach.
  Qiang Liu, Feng Yu, Shu Wu, Liang Wang. ACM Transactions on Intelligent Systems and Technology (TIST), 2018. [bib]

  @article{liu2018mining,
    title={Mining significant microblogs for misinformation identification: an attention-based approach},
    author={Liu, Qiang and Yu, Feng and Wu, Shu and Wang, Liang},
    journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
    volume={9},
    number={5},
    pages={1--20},
    year={2018},
    publisher={ACM New York, NY, USA}
  }
  

2017

• A Convolutional Approach for Misinformation Identification.
  Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. International Joint Conference on Artificial Intelligence (IJCAI), 2017. [bib]

  @inproceedings{yu2017convolutional,
    title={A Convolutional Approach for Misinformation Identification.},
    author={Yu, Feng and Liu, Qiang and Wu, Shu and Wang, Liang and Tan, Tieniu and others}
  }
  

• DeepStyle: Learning User Preferences for Visual Recommendation.
  Qiang Liu, Shu Wu, Liang Wang. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2017. [bib]

  @inproceedings{liu2017deepstyle,
    title={Deepstyle: Learning user preferences for visual recommendation},
    author={Liu, Qiang and Wu, Shu and Wang, Liang},
    booktitle={Proceedings of the 40th international acm sigir conference on research and development in information retrieval},
    pages={841--844},
    year={2017}
  }
  

• Multi-behavioral Sequential Prediction with Recurrent Log-bilinear Mode.
  Qiang Liu, Shu Wu, Liang Wang. IEEE Transactions on Knowledge and Data Engineering(TKDE), 2017. [bib]

  @article{liu2017multi,
    title={Multi-behavioral sequential prediction with recurrent log-bilinear model},
    author={Liu, Qiang and Wu, Shu and Wang, Liang},
    journal={IEEE Transactions on Knowledge and Data Engineering},
    volume={29},
    number={6},
    pages={1254--1267},
    year={2017},
    publisher={IEEE}
  }
  

• Unified Subspace Learning for Incomplete and Unlabeled Multi-view Data.
  Qiyue Yin, Shu Wu, Liang Wang. Pattern Recognition(PR), 2017. [bib]

  @article{yin2017unified,
    title={Unified subspace learning for incomplete and unlabeled multi-view data},
    author={Yin, Qiyue and Wu, Shu and Wang, Liang},
    journal={Pattern Recognition},
    volume={67},
    pages={313--327},
    year={2017},
    publisher={Elsevier}
  }
  

• Blood Pressure Prediction via Recurrent Models with Contextual Layer.
  Xiaohan Li, Shu Wu, Liang Wang. International World Wide Web Conference (WWW), 2017. [bib]

  @inproceedings{li2017blood,
    title={Blood pressure prediction via recurrent models with contextual layer},
    author={Li, Xiaohan and Wu, Shu and Wang, Liang},
    booktitle={Proceedings of the 26th International Conference on World Wide Web},
    pages={685--693},
    year={2017}
  }
  

2016

• Contextual Operation for Recommender Systems.
  Shu Wu, Qiang Liu, Liang Wang, Tieniu Tan. IEEE Transactions on Knowledge and Data Engineering(TKDE), 2016. [bib]

  @article{wu2016contextual,
    title={Contextual operation for recommender systems},
    author={Wu, Shu and Liu, Qiang and Wang, Liang and Tan, Tieniu},
    journal={IEEE Transactions on Knowledge and Data Engineering},
    volume={28},
    number={8},
    pages={2000--2012},
    year={2016},
    publisher={IEEE}
  }
  

• Coupled Topic Model for Collaborative Filtering with User-Generated Content.
  Shu Wu, Weiyu Guo, Song Xu, Yongzhen Huang, Liang Wang. IEEE Transactions on Human-Machine Systems(THMS), 2016. [bib]

  @article{wu2016coupled,
    title={Coupled topic model for collaborative filtering with user-generated content},
    author={Wu, Shu and Guo, Weiyu and Xu, Song and Huang, Yongzhen and Wang, Liang and Tan, Tieniu},
    journal={IEEE Transactions on Human-Machine Systems},
    volume={46},
    number={6},
    pages={908--920},
    year={2016},
    publisher={IEEE}
  }
  

• Context-aware Sequential Recommendation.
  Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li, Liang Wang. International Conference on Data Mining(ICDM), 2016. [bib]

  @inproceedings{liu2016context,
    title={Context-aware sequential recommendation},
    author={Liu, Qiang and Wu, Shu and Wang, Diyi and Li, Zhaokang and Wang, Liang},
    booktitle={2016 IEEE 16th International Conference on Data Mining (ICDM)},
    pages={1053--1058},
    year={2016},
    organization={IEEE}
  }
  

• Personalized Ranking with Pairwise Factorization Machines.
  Weiyu Guo, Shu Wu, Liang Wang, Tieniu Tan. Neurocomputing, 2016. [bib]

  @article{guo2016personalized,
    title={Personalized ranking with pairwise factorization machines},
    author={Guo, Weiyu and Wu, Shu and Wang, Liang and Tan, Tieniu},
    journal={Neurocomputing},
    volume={214},
    pages={191--200},
    year={2016},
    publisher={Elsevier}
  }
  

• A Dynamic Recurrent Model for Next Basket Recommendation.
  Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2016. [bib]

  @inproceedings{yu2016dynamic,
    title={A dynamic recurrent model for next basket recommendation},
    author={Yu, Feng and Liu, Qiang and Wu, Shu and Wang, Liang and Tan, Tieniu},
    booktitle={Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval},
    pages={729--732},
    year={2016}
  }
  

• Information Credibility Evaluation on Social Media.
  Shu Wu, Qiang Liu, Yong Liu, Liang Wang, Tieniu Tan. The Association for the Advancement of Artificial Intelligence (AAAI Demo), 2016. [bib]

  @inproceedings{wu2016information,
    title={Information credibility evaluation on social media},
    author={Wu, Shu and Liu, Qiang and Liu, Yong and Wang, Liang and Tan, Tieniu},
    booktitle={Proceedings of the AAAI conference on artificial intelligence},
    volume={30},
    number={1},
    year={2016}
  }
  

• Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts.
  Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. The Association for the Advancement of Artificial Intelligence (AAAI), 2016. [bib]

  @inproceedings{liu2016predicting,
    title={Predicting the next location: A recurrent model with spatial and temporal contexts},
    author={Liu, Qiang and Wu, Shu and Wang, Liang and Tan, Tieniu},
    booktitle={Proceedings of the AAAI conference on artificial intelligence},
    volume={30},
    number={1},
    year={2016}
  }
  

2015

• Incomplete Multi-view Clustering via Subspace Learning.
  Qiyue Yin, Shu Wu, Liang Wang. ACM International Conference on Information and Knowledge Management (CIKM), 2015. [bib]

  @inproceedings{yin2015incomplete,
    title={Incomplete multi-view clustering via subspace learning},
    author={Yin, Qiyue and Wu, Shu and Wang, Liang},
    booktitle={Proceedings of the 24th ACM international on conference on information and knowledge management},
    pages={383--392},
    year={2015}
  }
  

• Collaborative Prediction for Multi-entity Interaction with Hierarchical Representation.
  Qiang Liu, Shu Wu, Liang Wang. ACM International Conference on Information and Knowledge Management (CIKM), 2015. [bib]

  @inproceedings{liu2015collaborative,
    title={Collaborative prediction for multi-entity interaction with hierarchical representation},
    author={Liu, Qiang and Wu, Shu and Wang, Liang},
    booktitle={Proceedings of the 24th ACM International on Conference on Information and Knowledge Management},
    pages={613--622},
    year={2015}
  }
  

• Social-Relational Topic Model for Social Networks.
  Weiyu Guo, Shu Wu, Liang Wang, Tieniu Tan. ACM International Conference on Information and Knowledge Management (CIKM), 2015. [bib]

  @inproceedings{guo2015social,
    title={Social-relational topic model for social networks},
    author={Guo, Weiyu and Wu, Shu and Wang, Liang and Tan, Tieniu},
    booktitle={Proceedings of the 24th ACM International on Conference on Information and Knowledge Management},
    pages={1731--1734},
    year={2015}
  }
  

• A Convolutional Click Prediction Model.
  Qiang Liu, Feng Yu, Shu Wu, Liang Wang. ACM International Conference on Information and Knowledge Management (CIKM), 2015. [bib]

  @inproceedings{liu2015convolutional,
    title={A convolutional click prediction model},
    author={Liu, Qiang and Yu, Feng and Wu, Shu and Wang, Liang},
    booktitle={Proceedings of the 24th ACM international on conference on information and knowledge management},
    pages={1743--1746},
    year={2015}
  }
  

• Personalized Semantic Ranking for Collaborative Recommendation.
  Song Xu, Shu Wu, Liang Wang. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2015. [bib]

  @inproceedings{xu2015personalized,
    title={Personalized semantic ranking for collaborative recommendation},
    author={Xu, Song and Wu, Shu and Wang, Liang},
    booktitle={Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval},
    pages={971--974},
    year={2015}
  }
  

• COT: Contextual Operating Tensor for Context-aware Recommender Systems.
  Qiang Liu, Shu Wu, Liang Wang. The Association for the Advancement of Artificial Intelligence (AAAI Oral), 2015. [bib]

  @inproceedings{liu2015cot,
    title={Cot: Contextual operating tensor for context-aware recommender systems},
    author={Liu, Qiang and Wu, Shu and Wang, Liang},
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    volume={29},
    number={1},
    year={2015}
  }