网络内容分析与安全
数据挖掘

Google Scholar

https://scholar.google.com/citations?user=qVge6YYAAAAJ&hl=en

Code

https://github.com/CRIPAC-DIG

PyGCL: Graph Contrastive Learning Library for PyTorch

https://github.com/GraphCL/PyGCL

希望能与有科研理想的优秀同学一起合作，以前瞻的有价值的项目为载体，以一流的科研或应用成果为目标。

特别欢迎动手能力强、好奇心强、踏实勤奋的同学。

另中心招聘博士后和访问学生，欢迎联系。

081203-计算机应用技术

网络内容分析与安全
数据挖掘

2007-09--2012-07   加拿大舍布鲁克大学   计算机科学，博士
2004-09--2007-07   厦门大学   计算机应用专业，硕士
2000-09--2004-07   湖南大学   计算机科学与技术，学士

   

2016-11~现在, 中国科学院模式识别国家重点实验室, 副研究员
2012-09~2016-10,中国科学院模式识别国家重点实验室, 助理研究员

2022-01-01-今,ACM高级会员,
2021-10-01-今,IEEE高级会员,
2018-01-01-今,CCF高级会员,
2017-08-01-今,中国人工智能学会模式识别专委会委员,

   

Fenyu Hu, Liping Wang, Shu Wu, Qiang Liu, Liang Wang, Tieniu Tan, GraphDIVE: Graph Classification by Mixture of Diverse Experts, In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI), 2022.​

Junfei Wu, Qiang Liu, Weizhi Xu, and Shu Wu, Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention, In SIGIR, 2022.

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

(2) Weizhi Xu, Junfei Wu, Qiang Liu, Shu Wu, Liang Wang, Evidence-aware Fake News Detection with Graph Neural Networks, In Proceedings of the 31th International World Wide Web Conference (WWW), 2022.

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

(4)  Zeyu Cui, Feng Yu, Shu Wu, Qiang Liu, Disentangled Item Representation for Recommender Systems, ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2021.

(5) Fenyu Hu, Yanqiao Zhu, Shu Wu, Weiran Huang, Liang Wang, Tieniu Tan, GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction, Pattern Recognition (PR), 2021.

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

(7) Yujia Zheng, Siyi Liu, Zekun Li, Shu Wu, Cold-start Sequential Recommendation via Meta Learner, In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021.

(8) Xueli Yu, Weizhi Xu, Zeyu Cui, Shu Wu, Liang Wang. Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval, In Proceedings of the 30th International World Wide Web Conference (WWW), 2021.

(9) Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang. Graph Contrastive Learning with Adaptive Augmentation, In Proceedings of the 30th International World Wide Web Conference (WWW), 2021.

(10) Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Shuhui Wang, Liang Wang, Mining Latent Structures for Multimedia Recommendation, In MM, 2021.

(11) Yichen Xu, Yanqiao Zhu, Feng Yu, Qiang Liu, Shu Wu, Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction, In CIKM, 2021.

(12) Qiang Liu, Yanqiao Zhu, Zhaocheng Liu, Yufeng Zhang, Shu Wu, Deep Active Learning for Text Classification with Diverse Interpretations, In CIKM, 2021.

(13) Liping Wang, Fenyu Hu, Shu Wu, Liang Wang, Fully Hyperbolic Graph Convolution Network for Recommendation, In CIKM, 2021.

(14) Liping Wang, Fenyu Hu, Shu Wu, Liang Wang, Label-informed Graph Structure Learning for Node Classification, In CIKM, 2021.

(15) Qilong Yan, Yufeng Zhang, Qiang Liu, Shu Wu, Liang Wang, Relation-aware Heterogeneous Graph for User Profiling, In CIKM, 2021.

(16) Zeyu Cui, Yinjiang Cai, Shu Wu, Xibo Ma, Liang Wang, Motif-aware Sequential Recommendation, In SIGIR, 2021.

(17) Mengqi Zhang, Shu Wu, Xin Jiang, Ke Xu, Liang Wang. Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.

(18) Feng Yu, Zhaocheng Liu, Qiang Liu, Haoli Zhang, Shu Wu, Liang Wang. Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions, In Proceedings of 29th ACM International Conference on Information and Knowledge Management (CIKM), 2020.

(19) Xiaohan Li, Mengqi Zhang, Shu Wu, Zheng Liu, Liang Wang, Philip S Yu, Dynamic Graph Collaborative Filtering, In Proceedings of the 20th IEEE International Conference on Data Mining (ICDM), 2020.

(20) Shu Wu, Feng Yu, Xueli Yu, Qiang Liu, Liang Wang, Tieniu Tan, Jie Shao and Fan Huang. TFNet: Multi-Semantic Feature Interaction for CTR Prediction. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020.

(21) Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang and Tieniu Tan. TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020.

(22) Yufeng Zhang, Xueli Yu, Zeyu Cui, Shu Wu, Zhongzhen Wen, Liang Wang. Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks. In Proceedings of the 2020 Annual Conference of the Association for Computational Linguistics (ACL), 2020.

(23) Yanbei Liu, Xiao Wang, Shu Wu, Zhitao Xiao. Independence Promoted Graph Disentangled Networks. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.

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

(25) Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, Liang Wang. Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), 2019.

(26) Jingyi Wang, Qiang Liu, Zhaocheng Liu, Shu Wu. Towards Accurate and Interpretable Sequential Prediction: A CNN & Attention-Based Feature Extractor. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), 2019.

(27) Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan. Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019.

(28) Qiang Cui, Shu Wu, Yan Huang, Liang Wang. A Hierarchical Contextual Attention-based Network for Sequential Recommendation. Neurocomputing, 2019.

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

(30) Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, Liang Wang. Semi-supervised Compatibility Learning across Categories for Clothing Matching. In proceedings of the IEEE International Conference on Multimedia & Expo (ICME), 2019.

(31) Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. Attention-based Convolutional Approach for Misinformation Identification from Massive and Noisy Microblog Posts. Computers & Security, 2019.

(32) Zeyu Cui, Zekun Li, Shu Wu, Xiaoyu Zhang, Liang Wang. Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks. In Proceedings of the 28th International World Wide Web Conference (WWW), 2019.

(33) Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan. Session-based Recommendation with Graph Neural Network. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019.

(34) Qiang Cui, Yuyuan Tang, Shu Wu, Liang Wang. Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction. The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2019.

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

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

(37) Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. A Convolutional Approach for Misinformation Identification. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017.

(38) Qiang Liu, Shu Wu, Liang Wang. DeepStyle: Learning User Preferences for Visual Recommendation. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2017.

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

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

(41) Xiaohan Li, Shu Wu, Liang Wang. Blood Pressure Prediction via Recurrent Models with Contextual Layer. In Proceedings of the 26th International World Wide Web Conference (WWW), 2017.

(42) Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li, Liang Wang. Context-aware Sequential Recommendation. In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2016. 

(43) Weiyu Guo, Shu Wu, Liang Wang, Tieniu Tan. Personalized Ranking with Pairwise Factorization Machines. Neurocomputing, 2016.

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

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

(46) Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. A Dynamic Recurrent Model for Next Basket Recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2016.

(47) Shu Wu, Qiang Liu, Yong Liu, Liang Wang, Tieniu Tan. Information Credibility Evaluation on Social Media. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI, Demo), 2016.

(48) Shu Wu, Qiang Liu, Ping Bai, Liang Wang, Tieniu Tan. SAPE: A System for Situation-Aware Public Security Evaluation. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI, Demo), 2016.

(49) Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI), 2016.

(50) Qiyue Yin, Shu Wu, Liang Wang. Incomplete Multi-view Clustering via Subspace Learning. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015.

(51) Qiang Liu, Shu Wu, Liang Wang. Collaborative Prediction for Multi-entity Interaction with Hierarchical Representation. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015.

(52) Weiyu Guo, Shu Wu, Liang Wang, Tieniu Tan. Social-Relational Topic Model for Social Networks. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015.

(53) Qiang Liu, Feng Yu, Shu Wu, Liang Wang. A Convolutional Click Prediction Model. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015.

(54) Song Xu, Shu Wu, Liang Wang. Personalized Semantic Ranking for Collaborative Recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2015.

(55) Qiang Liu, Shu Wu, Liang Wang. COT: Contextual Operating Tensor for Context-aware Recommender Systems. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), 2015. 

(56) Shu Wu and Shengrui Wang. Information-theoretic Outlier Detection for large-scale Categorical Data. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2013.

(57) Shu Wu and Shengrui Wang. Rating-based Collaborative Filtering Combined with Additional Regularization. In Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2011.

(1)  吴书，王亮，王海滨，纪文峰，一种基于分解机和图神经网络的点击率预测方法及系统，申请号：CN202011435220.9

(2)  吴书，王亮，崔泽宇，王海滨，李凯，一种基于动态图卷积神经网络的社交关系预测方法及系统，申请号：CN202011439699.3

(3)  吴书，黄婷婷，杨敏，对象推荐方法、装置、存储介质及计算机设备，申请号：CN201810601391.0

(4)  吴书，王亮，谭铁牛，一种数据特征选择和预测方法及装置，授权号：CN106777891B

(5)  吴书，王亮，于雪莉，王海滨，纪文峰，李凯，基于深度神经网络与图网络的核心用户挖掘方法及系统，授权号：CN109597844B

(6)  谭铁牛，王亮，吴书，余峰，刘强，基于卷积神经网络的显著信息检测方法及装置，授权号：CN106844765B

(7)  王亮，吴书，崔强，刘强，一种基于多视角数据和循环网络构建序列预测模型的方法，授权号：CN106600347B

(8)  王亮，谭铁牛，吴书，刘强，基于高阶用户偏好的推荐方法，授权号：CN105069140B

(9)  王亮，吴书，徐松，一种基于耦合主题模型的协同滤波方法，授权号：CN103903163B

(10)王亮，吴书，王保兴，一种基于时序数据的推荐系统攻击检测算法，授权号：CN103678709B

(11)王亮，吴书，尹奇跃，一种基于结构约束的半监督多视角聚类方法，授权号：CN105718950B

(12)王亮，吴书，尹奇跃，一种基于矩阵分解的部分标注图像聚类方法及装置，授权号：CN105740881B

(13)王亮，吴书，尹奇跃，一种基于子空间学习的不完整跨模态检索方法，授权号：CN106844518B

(14)谭铁牛，王亮，吴书，郭韦昱，一种基于用户偏好的自适应采样方法，授权号：CN105740327B

(15)谭铁牛，王亮，吴书，刘强，余峰，一种基于动态周期神经网络的商品推荐方法，授权号：CN105844508B

(1)  MSRA StarTrack 微软“铸星计划”学者；

(2)   阿里巴巴Research Fellow；

(3)   CCF-腾讯犀牛鸟基金；

(4)  CCF-启明星辰鸿雁科研计划；

(5)  Selected Best Paper，UIC 2010，Xi'an，2010；

(6)  Scholarship奖学金，中国留学基金委（China Scholarship Council），2007-2010 ;

(7)  免高奖，加拿大魁北克省-中国留学基金委，2007-2010;

(8)  舍布鲁克大学Fellowship奖学金，2007-2011;

   

（1） Context-Aware Collaborative Prediction, Springer Briefs in Computer Science, Springer 2017, 2018-03, 第 1 作者

   

(1) 社会大数据跨尺度系统学习关键技术与示范应用，主要骨干，国家级，2022-01--2025-12

(2) 社交网络虚假媒体内容检测识别的理论与方法, 主持子课题，国家级，2020-01--2023-12

(3) 基于华为【Atlas800训练服务器】训练深度学习模型-NRE合作项目, 主持子课题，院级，2020-11--2021-11

(4) 分类用户服务的科技资源精准推荐技术，主持子课题，国家级，2019-07--2022-06

(5) 融合实体特征和序列信息的用户行为建模方法研究，主持，国家级，2018-01--2021-12

(6)  融合实体和交互上下文信息的社会化推荐方法研究，主持，国家级，2015-01--2017-12

​(7)  基于特征融合和序列分析的用户行为建模方法研究，主持，省级，2018-01--2020-12

(8)  中科院自动化所-京东金融智能金融风险联合实验室，主持，院级，2018-01--2020-12

(9)  手游玩家兴趣画像及生命周期研究，主持，院级，2018-01--2018-06

(10)  CCF-启明星辰鸿雁科研计划：基于深度自编码网络和稀疏表达的异常行为检测框架，主持，院级，2016-10--2017-09

(11)  CCF-腾讯犀牛鸟基金: 基于深度循环神经网络模型的点击通过率预测，主持，省级，2016-10--2017-09

(12)  九安医疗Collaborative Prediction of High Blood Pressure with Contextual Information，主持，院级，2016-01--2016-06

朱彦樵 硕士 UCLA博士

李泽坤 硕士 UCSB博士生

崔泽宇 博士 阿里巴巴达摩院

余峰 博士 阿里巴巴

崔强 博士 美团

刘强 博士 2016年微软学者奖学金（共10名博士生） 瑞莱智慧联合创始人&研发总监

尹奇跃 博士 中国科学院自动化所 副研究员 硕士生导师

郭韦昱 博士 中央财经大学信息学院 副教授

徐松 硕士 京东

本科实习生

​郑雨嘉 CMU硕士生

李霄寒 UIC博士生

王越乔 CMU硕士

钟闻 USC硕士