石川

Shi Chuan

(C. Shi)

Ph.D., Professor
Founder of Graph Data Mining and Machine Learning Lab (GAMMA Lab)
School of Computer Science
Beijing University of Posts and Telecommunications
P.O.Box 179, Beijing, P.R.China.100876
Tel: (86) 10-62283698
Email: shichuan (at) bupt.edu.cn
WeChat Official Account: GAMMA Lab
[Download CV in pdf]  [切换至中文页面]

Brief Bio

I'm the professor and PhD supervisor in School of Computer Sciences of Beijing University of Posts and Telecommunications, deputy director of Beijing Key Lab of Intelligent Telecommunication Software and Multimedia. The main research interests include data mining, machine learning, artificial intelligence and big data analysis. I have published more than 100 refereed papers, including top journals and conferences in data mining and machine learning, such as IEEE TKDE, ACM TKDD, KDD, WWW, NeurIPS, AAAI and IJCAI. I have been honored as the best paper award in ADMA 2011/ADMA 2018 and the best paper nomination in the WebConf 2021. I also won several awards, such as the second prize of Natural Science of Beijing/CCF (1st), the first prize of artificial intelligence science and technology progress of Wu Wenjun (3rd) and the first prize of science and technology progress of the Chinese Institute of Electronics (1st).

Research Interests

• Data mining and machine learning, especially focusing on graph data, including:
  • Heterogeneous information network analysis
  • Graph neural network
  • Recommender system
  • Knowledge graph
  • Multi-objective machine learning

Education

• Department of Computer Sciences, University of Illinois at Chicago
Oct. 2010 - Oct. 2011

Post-doctor in Data Mining;    Advisor: Prof. Philip S. Yu
• Institute of Computing Technology, Chinese Academy of Sciences
Sep. 2004 - Jun. 2007

Ph.D in Computer Software and Theory;    Advisor: Prof. Zhongzhi Shi
• School of Computer Sciences, Wuhan University
Sep. 2001 - Jun. 2004

M.Sc in Computer Software and Theory;    Advisor: Prof. Lishan Kang
• School of Computer Sciences, Jilin University
Sep. 1997 - Jun. 2001

B.S in Computer Science and Technology

Academic Experience

• Professor School of Computer Sciences, Beijing University of Posts and Telecommunications, Beijing, China
Nov. 2014 – Present
• Associate Professor School of Computer Sciences, Beijing University of Posts and Telecommunications, Beijing, China
Nov. 2009 – Nov. 2014
• Assistant Professor School of Computer Sciences, Beijing University of Posts and Telecommunications, Beijing, China
Jun. 2007 – Nov. 2009

Services and Talking

Talks

1. WWW24_Tutorial: Towards Graph Foundation Models [Part I] [Part II] [Part III]
2. Graph Structure Learning [pdf]
3. 图神经网络的设计空间与开源实践 [pdf]
4. 开放环境下图神经网络与应用 [pdf]
5. Graph Pre-training [pdf]
6. Learning Mechanism of Graph Neural Network [pdf]
7. ADL22-异质图神经网络: 模型、预训练与应用 [Introduction] [Model] [Pretraining] [Application] [Conclusion]
8. KDD21_Tutorial: Heterogeneous Graph Neural Network Concepts, Models and Applications [pdf]
9. CIKM19_Tutorial: Recent Developments of Deep Heterogeneous Information Network Analysis [Part I] [Part II] [Part III] [Part IV] [Part V]
10. 基于异质信息网络的推荐讲习报告: Heterogeneous Information Network based Recommendation [pdf]

Services

• Organizer of a serial of HENA workshops ("Heterogeneous information network analysis and applications") held by KDD21, CIKM19 et al.
• Organizer of a serial of MuNFA workshops (“Multi-view Network Fusion and Analysis”) held by IEEE DSC.
• Organizer of a serial of Social Network Analysis and Mining workshops held by CCDM and CCFAI.
• PC Chair of SMP2021, Area Chair of SMP2017 and CCKS19.
• Workshop in Netscix 2018: Invited talk: Heterogeneous information network based recommendation.
• CCF ADL 2017: Invited talk: Heterogeneous Information Network Modelling and Analysis.
• Social Computing Submit 2016: Keynote speech "Heterogeneous Information Network Analysis and Applications".
• ECOLE 2015: Keynote speech "Evolutionary Multi-objective Machine Learning".
• IEEE / ACM Member, Member of China Computer Federation; Senior Member of Chinese Association for Artificial Intelligence.
• Youth AE of SCI journal "Frontiers of Computer Science", Initial Editor of 《Annual Review of Artificial Intelligence》.
• Reviewer of international journals including IEEE TPAMI，IEEE TKDE, IEEE TKDD, IEEE TPDS, ACM TIST, PLOS et al.; (senior) PC Member of conferences including KDD, WWW, ACL, AAAI, IJCAI et al.; Reviewer of NFSC proposals.

Open-source Projects

1. All open-source code for publications [website]
2. OpenHGNN: OpenHGNN is an open source heterogeneous graph neural network toolkit based on Deep Graph Library (DGL) and PyTorch, which integrates SOTA models for heterogeneous graphs. OpenHGNN provides easy-to-use and extensible APIs, helping researchers and engineers to conduct research and applications on heterogeneous graphs fast. [website]
3. GammaGL: GammaGL is a multi-backend library for Graph Neural Networks, which supports TensorFlow, PyTorch, PaddlePaddle and MindSpore. Users can run the same code on different deep learning backends and use tensor-centric programming style like Pytorch Geometric (PyG). [website]

Selected Publications

[Sort by Topic]

Books

1. 石川, 王啸, Philip S. Yu. 异质图表示学习与应用. 机械工业出版社. ISBN 9787111711384. 2022.
2. 石川, 杨成（译）. 神经网络与深度学习. 机械工业出版社. ISBN 978711686859. 2021.
3. Chuan Shi, Xiao Wang, Cheng Yang. Advances in Graph Neural Networks. Springer. 978-3-031-16173-5. 2022. Preprint. [preface] [ch1] [ch2] [ch3] [ch4] [ch5] [ch6] [ch7] [ch8] [ch9]
4. Chuan Shi, Xiao Wang, Philip S. Yu. Heterogeneous Graph Representation Learning and Applications. Springer. 978-981-16-6166-2. 2022. Preprint. [preface] [ch1] [ch2] [ch3] [ch4] [ch5] [ch6] [ch7] [ch8] [ch9] [ch10] [ch11]
5. Chuan Shi, Philip S. Yu. Heterogeneous Information Network Analysis and Applications. Springer. ISBN 978-3-319-56211-7. 2017. [preface] [ch1] [ch2] [ch3] [ch4] [ch5] [ch6] [ch7] [ch8] [ch9]
6. Cheng Yang，Zhiyuan Liu，Cunchao Tu，Chuan Shi，Maosong Sun. Network Embedding: Theories, Methods, and Applications. Morgan & Claypool Publishers. ISBN-13:‎ 978-1636390444. 2021.3.
7. 胡琳梅，石川（译）. 异质信息网络分析与应用. 机械工业出版社. 2021.3.
8. 林旺群，金松昌，石川著. 大规模社会网络社区发现并行计算理论与方法. 兵器工业出版社. 2021.7.
9. 石川，王啸，胡琳梅 编著. 数据科学导论. 清华大学出版社. 2021.4.
10. 杨娟, 石川, 王柏. 形式语言与自动机(第二版). 北京邮电大学出版社. ISBN 978-7-5635-4997-9. 2017.
11. 牛温佳, 刘吉强, 石川. 用户网络行为画像. 中国工信出版集团 & 电子工业出版社. ISBN 978-7-121-28070-2. 2016.

Book Chapters

1. Chuan Shi. Heterogeneous graph neural network, book chapter in “Graph Neural Networks: Foundations, Frontiers, and Applications”. Springer. ISBN: 978-981-16-6054-2. 2022. [Chapter16]
2. Chuan Shi. Chapter 24: Evolutionary Multi-Objective Optimization for Supervised Learning. In: Ying Tan. Swarm Intelligence - From Concepts to Applications. IET. ISBN 978-1-78561-313-5. 2017. [book info]

Papers[Google Scholar][DBLP]

1. Xin Li, Weize Chen, Qizhi Chu, Haopeng Li, Zhaojun Sun, Ran Li, Chen Qian, Yiwei Wei, Zhiyuan Liu, Chuan Shi, Maosong Sun, Cheng Yang. Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models. NeurIPS 2024 [pdf] [code] [dataset]
2. Jiawei Liu, Jianwang Zhai, Mingyu Zhao, Zhe Lin, Bei Yu, Chuan Shi. PolarGate: Breaking the Functionality Representation Bottleneck of And-Inverter Graph Neural Network. ICCAD 2024 [pdf] [code & data]
3. Ruijia Wang, Haoran Dai, Cheng Yang, Le Song, Chuan Shi. Advancing Molecule Invariant Representation via Privileged Substructure Identification. KDD 2024 [pdf] [code & data]
4. Chuan Shi, Junze Chen, Jiawei Liu, Cheng Yang. Graph Foundation Model. Front. Comput. Sci. [pdf]
5. Zhiyuan Lu, Yuan Fang, Cheng Yang, Chuan Shi. Heterogeneous Graph Transformer with Poly-Tokenization. IJCAI 2024 [pdf] [code & data]
6. Yang Liu, Deyu Bo, Chuan shi. Graph Distillation with Eigenbasis Matching. ICML 2024 [pdf] [code & data]
7. Yujie Xing, Xiao Wang, Yibo Li, Hai Huang, Chuan Shi. Less is More: on the Over-Globalizing Problem in Graph Transformers. ICML 2024 [pdf] [code & data]
8. HongTao Cheng, Jiawei Liu, Jianwang Zhai, Mingyu Zhao, Cheng Yang, Chuan Shi. SATGL: An Open-source Graph Learning Toolkit for Boolean Satisfiability. ISEDA 2024 [pdf] [code & data]
9. Feng Guo, Jiawei Liu, Jianwang Zhai, Jingyu Jia, Kang Zhao, Chuan Shi. PGAU: Static IR Drop Analysis for Power Grid using Attention U-Net Architecture and Label Distribution Smoothing. GLSVLSI 2024 [pdf]
10. Mengmei Zhang, Mingwei Sun, Peng Wang, Shen Fan, Yanhu Mo, Xiaoxiao Xu, Hong Liu, Cheng Yang, Chuan Shi. GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks. WWW 2024 [pdf] [code & data]
11. Bo Yan, Yang Cao, Haoyu Wang, Wenchuan Yang, Junping Du, Chuan Shi. Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation. WWW 2024 [pdf] [code & data]
12. Zhongjian Zhang, Mengmei Zhang, Yue Yu, Cheng Yang, Jiawei Liu, Chuan Shi. Endowing Pre-trained Graph Models with Provable Fairness. WWW 2024 [pdf] [code & data]
13. Yibo Li, Xiao Wang, Yujie Xing, Shaohua Fan, Ruijia Wang, Yaoqi Liu, Chuan Shi. Graph Fairness Learning under Distribution Shifts. WWW 2024 [pdf] [code & data]
14. Cheng Yang, Chengdong Yang, Yawen Li, Zhiqiang Zhang, Jun Zhou, Chuan Shi. Calibrating Graph Neural Networks from a Data-centric Perspective. WWW 2024 [pdf] [code & data]
15. Yanhu Mo, Xiao Wang, Shaohua Fan, Chuan Shi. Graph Contrastive Invariant Learning from the Causal Perspective. AAAI 2024 [pdf] [code & data]
16. Tianrui Jia, Haoyang Li, Cheng Yang, Tao Tao, Chuan Shi. Graph Invariant Learning with Subgraph Co-mixup for Out-of-Distribution Generalization. AAAI 2024 [pdf] [code & data]
17. Yibo Li, Xiao Wang, Hongrui Liu, Chuan Shi. A Generalized Neural Diffusion Framework on Graphs. AAAI 2024 [pdf] [code & data]
18. Cheng Yang, Jixi Liu, Yunhe Yan, Chuan Shi. FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization. AAAI 2024 [pdf] [code & data]
19. Meiqi Zhu, Xiao Wang, Chuan Shi, Yibo Li, Junping Du. Towards Adaptive Information Fusion in Graph Convolutional Networks. TKDE 2023 [pdf]
20. Deyu Bo, Yuan Fang, Yang Liu, Chuan Shi. Graph Contrastive Learning with Stable and Scalable Spectral Encoding. NeurIPS 2023 [pdf] [code & data]
21. Ruijia Wang, Yiwu Sun, Yujie Luo, Shaochuan Li, Cheng Yang, Xingyi Cheng, Hui Li, Chuan Shi, Le Song. Injecting Multimodal Information into Rigid Protein Docking via Bi-level Optimization. NeurIPS 2023 [pdf]
22. Donglin Xia, Xiao Wang, Nian Liu, Chuan Shi. Learning Invariant Representations of Graph Neural Networks via Cluster Generalization. NeurIPS 2023 [pdf] [code & data]
23. Yue Yu, Xiao Wang, Mengmei Zhang, Nian Liu, Chuan Shi. Provable Training for Graph Contrastive Learning. NeurIPS 2023 [pdf] [code & data]
24. Yuanxin Zhuang, Chuan Shi, Mengmei Zhang, Jinghui Chen, Lingjuan Lyu, Pan Zhou, Lichao Sun. Unveiling the Secrets without Data: Can Graph Neural Networks Be Exploited through Data-Free Model Extraction Attacks. USENIX Security 2023 [pdf]
25. Jiawei Liu, Cheng Yang, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S. Yu, Chuan Shi. Towards Graph Foundation Models: A Survey and Beyond. arXiv 2023 [pdf]
26. Chuan Shi, Houye Ji, Zhiyuan Lu, Ye Tang, Pan Li, Cheng Yang. Distance Information Improves Heterogeneous Graph Neural Networks. TKDE 2023 [pdf] [code & data]
27. Shaohua Fan, Xiao Wang, Chuan Shi, Peng Cui, Bai Wang. Generalizing Graph Neural Networks on Out-Of-Distribution Graphs. TPAMI 2023 [pdf] [code & data]
28. Yijian Liu, Hongyi Zhang, Cheng Yang, Ao Li, Yugang Ji, Luhao Zhang, Tao Li, Jinyu Yang, Tianyu Zhao, Juan Yang, Hai Huang, Chuan Shi. Datasets and Interfaces for Benchmarking Heterogeneous Graph Neural Networks. CIKM 2023 [pdf] [code & data]
29. Fengqi Liang, Huan Zhao, Zhenyi Wang, Wei Fang, Chuan Shi. Retrieving GNN Architecture for Collaborative Filtering. CIKM 2023 [pdf] [code & data]
30. Zhenyi Wang, Huan Zhao, Fengqi Liang, Chuan Shi. Node-dependent Semantic Search over Heterogeneous Graph Neural Networks. CIKM 2023 [pdf] [code & data]
31. Jiawei Liu, Haihan Gao, Chuan Shi, Hongtao Cheng, Qianlong Xie. Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation. Applied Sciences 2023 [pdf]
32. Bo Yan, Cheng Yang, Chuan Shi, Yong Fang, Qi Li, Yanfang Ye, Junping Du. Graph Mining for Cybersecurity: A Survey. TKDD 2023 [pdf]
33. Chuan Shi, Xiao Wang, Cheng Yang, Qingyong Li, Bin Wu. 数据科学研究型人才培养的思考与实践 计算机教育 [pdf]
34. Shuyun Gu, Xiao Wang, Chuan Shi. Duplicate Multi-modal Entities Detection with Graph Contrastive Self-training Network. ECML-PKDD 2023 [pdf]
35. Yuxin Guo, Cheng Yang, Yuluo Chen, Jixi Liu, Chuan Shi, Junping Du. A Data-centric Framework to Endow Graph Neural Networks with Out-Of-Distribution Detection Ability. KDD 2023 [pdf] [code & data]
36. Yaoqi Liu, Cheng Yang, Tianyu Zhao, Hui Han, Siyuan Zhang, Jing Wu, Guangyu Zhou, Hai Huang, Hui Wang, Chuan Shi. GammaGL: A Multi-Backend Library for Graph Neural Networks. SIGIR 2023 [pdf] [code & data]
37. Deyu Bo, Chuan Shi, Lele Wang, Renjie Liao. Specformer: Spectral Graph Neural Networks meet Transformers. ICLR 2023 [pdf] [code & data]
38. Cheng Yang，Xumeng Gong，Chuan Shi，Philip S. Yu. A Post-Training Framework for Improving Heterogeneous Graph Neural Networks. WWW 2023 [pdf] [code & data]
39. Mengmei Zhang, Xiao Wang, Chuan Shi, Lingjuan Lyu, Tianchi Yang, Junping Du. Minimum Topology Attacks for Graph Neural Networks. WWW 2023 [pdf]
40. Tianchi Yang, Haihan Gao, Cheng Yang, Chuan Shi, Qianlong Xie, Xingxing Wang, Dong Wang. Memory-enhanced Period-aware Graph Neural Network for General POI Recommendation. DASFAA 2023 [pdf]
41. Bo Yan, Cheng Yang, Chuan Shi, Jiawei Liu, Xiaochen Wang. Abnormal Event Detection via Hypergraph Contrastive Learning. SDM 2023 [pdf] [code & data]
42. Cheng Yang, Yuxin Guo, Yao Xu, Chuan Shi, Jiawei Liu, Chunchen Wang, Xin Li, Ning Guo, Hongzhi Yin. Learning to Distill Graph Neural Networks. WSDM 2023 [pdf] [code & data]
43. Xumeng Gong, Cheng Yang, Chuan Shi. MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning. AAAI 2023 [pdf] [code & data]
44. Shaohua Fan, Shuyang Zhang, Xiao Wang, Chuan Shi. Directed Acyclic Graph Structure Learning from Dynamic Graphs. AAAI 2023 [pdf] [code & data]
45. Ruijia Wang, Xiao Wang, Chuan Shi, Le Song. Uncovering the Structural Fairness in Graph Contrastive Learning. NeurIPS 2022 [pdf] [code & data]
46. Nian Liu, Xiao Wang, Deyu Bo, Chuan Shi, Jian Pei. Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum. NeurIPS 2022 [pdf] [code & data]
47. Shaohua Fan, Xiao Wang, Yanhu Mo, Chuan Shi, Jian Tang. Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure. NeurIPS 2022 [pdf] [code & data]
48. Hui Han, Tianyu Zhao, Cheng Yang, Hongyi Zhang, Yaoqi Liu, Xiao Wang, Chuan Shi. OpenHGNN: An Open-Source Toolkit for Heterogeneous Graph Neural Networks. CIKM 2022 [pdf]
49. Jiawei Liu, Chuan Shi, Cheng Yang, Zhiyuan Lu, Philip S.Yu. A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources. AI Open 2022 [pdf]
50. Tianchi Yang, Cheng Yang, Luhao Zhang, Chuan Shi, Maodi Hu, Huaijun Liu, Tao Li, Dong Wang. Co-clustering Interactions via Attentive Hypergraph Neural Network. SIGIR 2022 [pdf] [code & data]
51. Yiding Zhang, Chaozhuo Li, Xing Xie, Xiao Wang, Chuan Shi, Yuming Liu, Hao Sun,Liangjie Zhang, Weiwei Deng, Qi Zhang. Geometric Disentangled Collaborative Filtering. SIGIR 2022 [pdf] [code & data]
52. Tianyu Zhao, Cheng Yang, Yibo Li, Quan Gan, Zhenyi Wang, Fengqi Liang, Huan Zhao,Yingxia Shao, Xiao Wang, Chuan Shi. Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network. SIGIR 2022 [pdf] [code & data]
53. Shuyun Gu , Xiao Wang , Chuan Shi, Ding Xiao. Self-supervised Graph Neural Networks for Multi-behavior Recommendation. IJCAI 2022 [pdf] [code & data]
54. Yuanxin Zhuang, Lingjuan Lyu, Chuan Shi, Carl Yang, Lichao Sun. Data-Free Adversarial Knowledge Distillation for Graph Neural Networks. IJCAI 2022 [pdf] [code & data]
55. Tianchi Yang, Luhao Zhang, Chuan Shi, Cheng Yang, Siyong Xu, Ruiyu Fang, Maodi Hu, Huaijun Liu, Tao Li, Dong Wang. Gated Hypergraph Neural Network for Scene-aware Recommendation. DASFAA 2022 [pdf] [code & data]
56. Yugang Ji, Guanyi Chu, Xiao Wang, Chuan Shi, Jianan Zhao, Junping Du. Prohibited Item Detection via Risk Graph Structure Learning. WWW 2022 [pdf]
57. Nian Liu, Xiao Wang, Lingfei Wu, Yu Chen, Xiaojie Guo,Chuan Shi. Compact Graph Structure Learning via Mutual Information Compression. WWW 2022 [pdf] [code & data]
58. Hongrui Liu, Binbin Hu, Xiao Wang, Chuan Shi, Zhiqiang Zhang, Jun Zhou. Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift. WWW 2022 [pdf] [code & data]
59. Shaohua Fan, Xiao Wang, Chuan Shi, Kun Kuang, Nian Liu, Bai Wang. Debiased Graph Neural Networks with Agnostic Label Selection Bias. TNNLS 2022 [pdf] [code & data]
60. Mengmei Zhang, Xiao Wang, Meiqi Zhu, Chuan Shi, Zhiqiang Zhang, Jun Zhou. Robust Heterogeneous Graph Neural Networks against Adversarial Attacks. AAAI 2022 [pdf] [code & data]
61. Deyu Bo, BinBin Hu, Xiao Wang, Zhiqiang Zhang, Chuan Shi, Jun Zhou. Regularizing Graph Neural Networks via Consistency-Diversity Graph Augmentations. AAAI 2022 [pdf] [code & data]
62. Zhenyi Wang, Huan Zhao,Chuan Shi. Profiling the Design Space for Graph Neural Networks based Collaborative Filtering. WSDM 2022 [pdf] [code & data]
63. Cheng Yang, Chunchen Wang, Yuanfu Lu, Xumeng Gong,Chuan Shi, Wei Wang, Xu Zhang. Few-shot Link Prediction in Dynamic Networks. WSDM 2022 [pdf] [code & data]
64. Xiao Wang, Hongrui Liu, Chuan Shi, Cheng Yang. Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration. NeurIPS 2021 [pdf] [code & data]
65. Yugang Ji, Chuan Shi, Xiao Wang. Prohibited Item Detection on Heterogeneous Risk Graphs. CIKM 2021 [pdf]
66. Xunqiang Jiang, Yuanfu Lu, Yuan Fang, Chuan Shi. Contrastive Pre-Training of GNNs on Heterogeneous Graphs. CIKM 2021 [pdf] [code & data]
67. Siyong Xu, Cheng Yang, Chuan Shi, Yuan Fang, Yuxin Guo, Tianchi Yang, Luhao Zhang, Maodi Hu. Topic-aware Heterogeneous Graph Neural Network for Link Prediction. CIKM 2021 [pdf] [code & data]
68. Hao Wang, Cheng Yang, Chuan Shi. Neural Information Diffusion Prediction with Topic-Aware Attention Network. CIKM 2021 [pdf] [code & data]
69. Linmei Hu, Tianchi Yang, Luhao Zhang, Wanjun Zhong, Duyu Tang,Chuan Shi, Nan Duan, Ming Zhou. Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge. ACL 2021 [pdf] [code & data]
70. Tianchi Yang, Linmei Hu, Chuan Shi, Houye Ji, Xiaoli Li, Liqiang Nie. HGAT: Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification. TOIS 2021 [pdf] [code & data]
71. Houye Ji, Xiao Wang, Chuan Shi, Bai Wang and Philip S. Yu. Heterogeneous Graph Propagation Network. TKDE 2021 [pdf] [code & data]
72. Houye Ji, Pan Li, Chuan Shi, and Cheng Yang. Heterogeneous Graph Neural Network with Distance Encoding. ICDM 2021 [pdf] [code & data]
73. 刘佳玮 , 石川, 杨成, 菲利普·俞. 基于异质信息网络的推荐系统研究综述. 信息安全学报. [pdf]
74. Ruijia Wang, Chuan Shi, Tianyu Zhao, Xiao Wang, Yanfang Ye.Heterogeneous Information Network Embedding with Adversarial Disentangler. TKDE 2021. [pdf] [code & data]
75. Xunqiang Jiang, Tianrui Jia, Yuan Fang,Chuan Shi,Zhe Lin,Hui Wang.Pre-training on Large-Scale Heterogeneous Graph. KDD 2021. [pdf] [code & data]
76. Xiao Wang, Nian Liu, Hui Han,Chuan Shi.Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning. KDD 2021. [pdf] [code & data]
77. Guanyi Chu, Xiao Wang, Chuan Shi,Xunqiang Jiang.CuCo: Graph Representation with Curriculum Contrastive Learning. IJCAI 2021. [pdf] [code & data]
78. Chen Li, Yuanfu Lu, Wei Wang,Chuan Shi, Ruobing Xie, Haili Yang, Cheng Yang,Xu Zhang, Leyu Lin .Package Recommendation with Intra- and Inter-Package Attention Networks. SIGIR 2021. [pdf] [code & data]
79. Linmei Hu, Mengmei Zhang, Shaohua Li, Jinghan Shi, Chuan Shi, Cheng Yang and Zhiyuan Liu.Text-Graph Enhanced Knowledge Graph Representation Learning. FCS 2021. [pdf]
80. Yugang Ji, Tianrui Jia, Yuan Fang, Chuan Shi.Dynamic Heterogeneous Graph Embedding via Heterogeneous Hawkes Process. ECML 2021. [pdf] [code & data]
81. Yuanxin Zhuang, Chuan Shi, Cheng Yang, Fuzhen Zhuang, and Yangqiu Song.Semantic-Specific Hierarchical Alignment Network for Heterogeneous Graph Adaptation. ECML 2021. [pdf] [code & data]
82. Yiding Zhang, Xiao Wang, Chuan Shi,Nian Liu, Guojie Song.Lorentzian Graph Convolutional Networks. WWW 2021. [pdf] [code & data]
83. Houye Ji, Junxiong Zhu, Chuan Shi, Xiao Wang, Bai Wang,Chaoyu Zhang,Zixuan Zhu ,Feng Zhang,Yanghua Li.Large-scale Comb-K Recommendation. WWW 2021. [pdf] [code & data]
84. Meiqi Zhu, Xiao Wang, Chuan Shi, Houye Ji, Peng Cui.Interpreting and Unifying Graph Neural Networks with An Optimization Framework. WWW 2021. [pdf] [slides] [code & data]
85. Cheng Yang, Jiawei Liu, Chuan Shi.Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework. WWW 2021. [pdf] [code & data]
86. Ruijia Wang, Shuai Mou, Xiao Wang∗, Wanpeng Xiao, Chuan Shi, Xing Xie.Graph Structure Estimation Neural Networks. WWW 2021. [pdf] [code & data]
87. Deyu Bo, Xiao Wang, Chuan Shi, Huawei Shen.Beyond Low-frequency Information in Graph Convolutional Networks. AAAI 2021. [pdf] [slides] [code & data]
88. Yuanfu Lu, Xunqiang Jiang, Yuan Fang, Chuan Shi.Learning to Pre-train Graph Neural Networks. AAAI 2021. [pdf] [code & data]
89. Jianan Zhao, Xiao Wang, Chuan Shi, Binbin Hu, Guojie Song, Yanfang Ye.Heterogeneous Graph Structure Learning for Graph Neural Networks. AAAI 2021. [pdf] [code & data]
90. Houye Ji, Junxiong Zhu, Xiao Wang, Chuan Shi, Bai Wang, Xiaoye Tan, Yanghua Li, Shaojian He.Who You Would Like to Share With? A Study of Share Recommendation in Social E-commerce. AAAI 2021. [pdf] [code & data]
91. Chen Li, Linmei Hu, Chuan Shi, Guojie Song, Yuanfu Lu.Sequence-aware Heterogeneous Graph Neural Collaborative Filtering. SDM 2021. [pdf] [code & data]
92. Mengmei Zhang, Linmei Hu,Chuan Shi, Xiao Wang.Adversarial Label-Flipping Attack and Defense for Graph Neural Networks. ICDM 2020. [pdf] [code & data]
93. Yugang Ji, Mingyang Yin, Hongxia Yang, Jingren Zhou, Vincent W Zheng, Chuan Shi, Yuan Fang.Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning via Importance Sampling. ACM TKDD 2020. [pdf]
94. Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, Philip S. Yu. A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources. Submitted to IEEE Transactions on Big Data. [pdf]
95. 石川, 王睿嘉, 王啸. 异质信息网络分析与应用综述. 软件学报. [pdf]
96. Xiao Wang, Shaohua Fan , Kun Kuang, Chuan Shi, Jiawei Liu, Bai Wang.Decorrelated Clustering with Data Selection Bias. IJCAI 2020. [pdf] [code & data]
97. Linmei Hu, Siyong Xu, Chen Li, Cheng Yang, Chuan Shi, Nan Duan, Xing Xie, Ming Zhou.Graph Neural News Recommendation with Unsupervised Preference Disentanglement. ACL 2020. [pdf] [code & data]
98. Yuanfu Lu, Yuan Fang, Chuan Shi.Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation. KDD 2020. [pdf] [code & data]
99. Yuanfu Lu, Ruobing Xie, Chuan Shi, Yuan Fang, Wei Wang, Xu Zhang, Leyu Lin.Social Influence Attentive Neural Network for Friend-Enhanced Recommendation. ECML-PKDD 2020. [pdf] [code & data]
100. Xiao Wang, Yuanfu Lu, Chuan Shi, Ruijia Wang, Peng Cui, Shuai Mao.Dynamic Heterogeneous Information Network Embedding with Meta-path based Proximity. TKDE 2020. [pdf] [code & data]
101. Chuan Shi, Yuanfu Lu, Linmei Hu, Zhiyuan Liu, Huadong Ma.RHINE: Relation Structure-Aware Heterogeneous Information Network Embedding TKDE 2020. [pdf] [code & data]
102. Jianan Zhao, Xiao Wang, Chuan Shi, Zekuan Liu, Yanfang Ye.Network Schema Preserving Heterogeneous Information Network Embedding. IJCAI 2020. [pdf] [code & data]
103. Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, Jian Pei.AM-GCN: Adaptive Multi-channel Graph Convolutional Networks. KDD 2020. [pdf] [code & data]
104. Yugang Ji, Chuan Shi, Yuan Fang, Xiangnan Kong, Mingyang Yin.Semi-supervised Co-Clustering on Attributed Heterogeneous Information Networks. IPM. [pdf] [code & data]
105. Yugang Ji, MingYang Yin, Yuan Fang, Hongxia Yang, Xiangwei Wang, Tianrui Jia, Chuan Shi.Temporal Heterogeneous Interaction Graph Embedding For Next-Item Recommendation. PKDD 2020. [pdf] [code & data]
106. Shaohua Fan, Xiao Wang, Chuan Shi, Emiao Lu, Ken Lin, Bai Wang.One2Multi Graph Autoencoder for Multi-view Graph Clustering. WWW 2020. [pdf] [code & data]
107. Deyu Bo, Xiao Wang, Chuan Shi, Meiqi Zhu, Emiao Lu, Peng Cui.Structural Deep Clustering Network. WWW 2020. [pdf] [code & data]
108. Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi, Bryan Hooi, He Huang, Xueqi Cheng.FlowScope: Spotting Money Laundering Based on Graphs. AAAI 2020. [pdf] [code & data]
109. Xiao Wang, Ruijia Wang, Chuan Shi, Guojie Song, Qingyong Li.Multi-Component Graph Convolutional Collaborative Filtering. AAAI 2020. [pdf] [code & data]
110. Xunqiang Jiang, Binbin Hu, Yuan Fang, Chuan Shi.Multiplex Memory Network for Collaborative Filtering. SDM 2020. [pdf] [code & data]
111. Linmei Hu, Chen Li, Chuan Shi, Cheng Yang, Chao Shao.Graph neural news recommendation with long-term and short-term interests modeling. IPM 2020. [pdf] [code & data]
112. Chuan Shi, Xiaotian Han, Li Song, Xiao Wang, Senzhang Wang, Junping Du, Philip S. Yu.Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks. TKDE 2019. [pdf]
113. Linmei Hu, Tianchi Yang, Chuan Shi, Houye Ji, Xiaoli Li.Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification. EMNLP 2019. [pdf] [code & data]
114. Linmei Hu, Luhao Zhang, Chuan Shi, Liqiang Nie, Weili Guan, Cheng Yang.Improving Distantly-Supervised Relation Extraction with Joint Label Embedding. EMNLP 2019. [pdf] [code & data]
115. Jianan Zhao, Ding Xiao, Linmei Hu, Chuan Shi.Coupled Semi-supervised Clustering : Exploring Attribute Correlations in Heterogeneous Information Networks. APWEB 2019. [pdf]
116. Yuyan Zheng, Chuan Shi, Xiangnan Kong, Yanfang Ye.Author Set Identification via Quasi-Clique Discovery. CIKM 2019. [pdf] [code] [data]
117. Yuanfu Lu, Xiao Wang, Chuan Shi, Philip S. Yu, Yanfang Ye.Temporal Network Embedding with Micro- and Macro-dynamics. CIKM 2019. [pdf] [code & data]
118. Binbin Hu, Yuan Fang, Chuan Shi.Adversarial Learning on Heterogeneous Information Networks. KDD 2019. [pdf] [code & data]
119. Yiming Zhang, Yujie Fan, Wei Song, Shifu Hou, Yanfang Ye, Xin Li, Liang Zhao, Chuan Shi, Jiabin Wang, Qi Xiong.Your Style Your Identity: Leveraging Writing and Photography Styles for Drug Trafficker Identification in Darknet Markets over Attributed Heterogeneous Information Network. WWW 2019. [pdf]
120. Yujie Fan, Yiming Zhang, Shifu Hou, Lingwei Chen, Yanfang Ye, Chuan Shi, Liang Zhao, Shouhuai Xu.iDev: Enhancing Social Coding Security by Cross-platform User Identification Between GitHub and Stack Overflow. IJCAI 2019. [pdf]
121. Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi, Linmei Hu, Biyu Ma, Yongliang Li.Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation. KDD 2019. [pdf] [code & data]
122. Yugang Ji, Chuan Shi, Fuzhen Zhuang, Philip S. Yu. Integrating Topic Model and Heterogeneous Information Network for Aspect Mining with Rating Bias. PAKDD 2019. [pdf]
123. Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, Philip S. Yu, Yanfang Ye.Heterogeneous Graph Attention Network. WWW 2019. [pdf] [code & data]
124. Xiao Wang, Yiding Zhang, Chuan Shi. Hyperbolic Heterogeneous Information Network Embedding. AAAI 2019. [pdf] [code & data]
125. Binbin Hu, Zhiqiang Zhang, Chuan Shi, Jun Zhou, Xiaolong Li, Yuan Qi. Cash-out User Detection based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism. AAAI 2019. [pdf]
126. Yuanfu Lu, Chuan Shi, Linmei Hu, Zhiyuan Liu. Relation Structure-Aware Heterogeneous Information Network Embedding. AAAI 2019. [pdf] [code & data]
127. Shaohua Fan, Chuan Shi, Xiao Wang. Abnormal Event Detection via Heterogeneous Information Network Embedding. CIKM 2018. [pdf] [code & data]
128. Binbin Hu, Chuan Shi, Wayne Xin Zhao, Tianchi Yang. Local and Global Information Fusion for Top-N Recommendation in Heterogeneous Information Network. CIKM 2018. [pdf] [code & data]
129. Li Song, Ruijia Wang, Ding Xiao, Xiaotian Han, Yanan Cai, Chuan Shi. Anomalous Trajectory Detection using Recurrent Neural Network. ADMA 2018. Best paper award [pdf] [code & data]
130. Xin Wan, Chen Li, Ruijia Wang, Ding Xiao, Chuan Shi. Abstractive Document Summarization via Bidirectional Decoder. AMDA 2018. [pdf] [code & data]
131. Menghao Zhang, Binbin Hu, Chuan Shi, Bin Wu, Bai Wang. Matrix Factorization meets Social Network Embedding for Rating Prediction. APWeb-WAIM 2018. [pdf]
132. Xiaotian Han, Chuan Shi, Lei Zheng, Philip S. Yu, Jianxin Li, Yuanfu Lu. Representation Learning with Depth and Breadth for Recommendation using Multi-view Data. APWeb-WAIM 2018. [pdf]
133. Xiaohuan Cao, Chuan Shi, Yuyan Zheng, Jiayu Ding, Xiaoli Li, Bin Wu. A Heterogeneous Information Network Method for Entity Set Expansion in Knowledge Graph. PAKDD 2018. [pdf]
134. Houye Ji, Chuan Shi. Attention Based Meta Path Fusion for Heterogeneous Information Network Embedding. PRICAI 2018. [pdf]
135. Yuyan Zheng, Chuan Shi, Xiaohuan Cao, Xiaoli Li, Bin Wu. A Meta Path based Method for Entity Set Expansion in Knowledge Graph. IEEE Transactions on Big Data, 2018. [pdf] [code & data]
136. Yuyan Zheng, Ying Tian, Chuan Shi. Method of Entity Set Expansion Based on Frequent Pattern under Meta Path. Journal of Software, 2018, 29(10):0. [pdf]
137. Xiaoji Chen, Chuan Shi, Aimin Zhou, Bin Wu. A Multiobjective Evolutionary Algorithm Based on Hybrid Individual Selection Mechanism. Journal of Software, 2018. [pdf]
138. Ding Xiao, Yugang Ji, Yitong Li. Fuzhen Zhuang, Chuan Shi. Coupled matrix factorization and topic modeling for aspect mining. Information Processing and Management, 2018. [pdf] [code & data]
139. Chuan Shi, Zhiqiang Zhang, Yugang Ji, Weipeng Wang, Philiph S. Yu, Zhiping Shi. SemRec: A Personalized Semantic Recommendation Method based on Weighted Heterogeneous Information Networks. World Wide Web, 2018: 1-32. [pdf] [code]
140. Chuan Shi, Jian Liu, Yiding Zhang, Binbin Hu, Shenghua Liu, and Philip S. Yu. MFPR: A Personalized Ranking Recommendation with Multiple Feedback. ACM Transactions on Social Computing, 2018. [pdf] [code]
141. Chuan Shi, Binbin Hu, Wayne Xin Zhao, Philip S. Yu. Heterogeneous Information Network Embedding for Recommendation. IEEE Transactions on Knowledge and Data Engineering, 2018. [pdf] [code & data]
142. Binbin Hu, Chuan Shi, Wayne Xin Zhao, Philip S. Yu. Leveraging Meta-path based Context for Top-N Recommendation with A Neural Co-Attention Model. KDD 2018. [pdf] [code & data]
143. Xiaotian Han, Chuan Shi, Senzhang Wang, Philip S. Yu, Li Song. Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks. IJCAI 2018. [pdf] [code & data]
144. Pudi Chen, Shenghua Liu, Chuan Shi, Bryan Hooi, Bai Wang, Xueqi Cheng. NeuCast: Seasonal Neural Forecast of Power Grid Time Series. IJCAI 2018. [pdf] [code & data]
145. Chuan Shi, Yizhou Sun. Research development of representation learning for heterogeneous network. Communications of the CCF. 2017.12. [pdf]
146. Chuan Shi, Yizhou Sun, Philip S. Yu. Current development and Future work of heterogeneous information network. Communications of the CCF. 2017.11. [pdf]
147. Chuan Shi, Binbin Hu, Wayne Xin Zhao, Philip S. Yu. Heterogeneous Information Network Embedding for Recommendation. arXiv:1711.10730, 2017. [pdf]
148. Fuzhen Zhuang, Zhiqiang Zhang, Mingda Qian, Chuan Shi, Xing Xie, Qing He. Representation learning via Dual-Autoencoder for recommendation. Neural Networks, 90, 83-89, 2017. [pdf]
149. Jing Zheng, Fuzhen Zhuang, Chuan Shi. Local Ensemble across Multiple Sources for Collaborative Filtering. CIKM 2017. [pdf]  [code & data]
150. Yang Xiao，Ding Xiao，Binbin Hu，Chuan Shi. ANE: Network Embedding via Adversarial Autoencoders. BigComp 2018. [pdf]
151. Xiaohuan Cao, Yuyan Zheng, Chuan Shi, Jingzhi Li, Bin Wu. Meta-path-based link prediction in schema-rich heterogeneous information network. International Journal of Data Science and Analytics, 2017. [pdf]
152. Jing Zheng, Jian Liu, Chuan Shi, Fuzhen Zhuang, Jingzhi Li, Bin Wu. Recommendation in heterogeneous information network via dual similarity regularization. International Journal of Data Science and Analytics, 2017. [pdf]
153. Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, Philip S. Yu. A survey on Heterogeneous Information Network Analysis. IEEE Transactions on Knowledge and Data Engineering, 29(1), 17-37, 2017 [pdf]
154. Jian Liu, Chuan Shi, Binbin Hu, Shenghua Liu, Philip S. Yu. Personalized Ranking Recommendation via Integrating Multiple Feedbacks. PAKDD 2017. [pdf]
155. Yuyan Zheng, Chuan Shi, Xiaohuan Cao, Xiaoli Li, Bin Wu. Entity Set Expansion with Meta Path in Knowledge Graph. PAKDD 2017. [pdf]
156. Chuan Shi, Yitong Li, Philip S. Yu, Bin Wu. Constrained-Meta-Path based Ranking in Heterogeneous Information Network. Knowledge and Information System, 49(2), 719-747, 2016. [pdf]
157. Chuan Shi, Jian Liu, Fuzheng Zhuang, Philip S. Yu, Bin Wu. Integrating Heterogeneous Information via Flexible Regularization Framework for Recommendation. Knowledge and Information System, 2016. [pdf]
158. Chuan Shi, Bowei He, Menghao Zhang, Fuzhen Zhuang, Philip S. Yu, Naiwang Guo. Expenditure Aware Rating Prediction for Recommendation. IEEE BigData 2016. [pdf]
159. Qiaolian Liu, Jianfei Zhao, Naiwang Guo, Ding Xiao, and Chuan Shi. High-Dimensional Data Visualization Based on User Knowledge. DMBD 2016. [pdf]
160. Yitong Li, Chuan Shi, Huidong Zhao, Fuzhen Zhuang, and Bin Wu. Aspect Mining with Rating Bias. ECML2016. [pdf]
161. Jiawei Hu, Zhiqiang Zhang, Jian Liu, Chuan Shi et al. RecExp: A semantic recommender system with explanation based on heterogeneous information network. RecSys 2016. [pdf]
162. Xiaohuan Cao, Yuyan Zheng, Chuan Shi, Bin Wu. Link Prediction in Schema-Rich Heterogeneous Information Network. PAKDD 2016. [pdf]
163. Jing Zheng, Jian Liu, Chuan Shi, Fuzheng Zhuang, Bin Wu. Dual Similarity Regularization for Recommendation. PAKDD 2016. [pdf]
164. Xiao Ding, Li Yi-Tong, Shi Chuan. Autonomic discovery of subgoals in hierarchical reinforcement learning. Journal of China Universities of Posts and Telecommunications, 21(5): 94–104, 2015. [pdf]
165. Chuan Shi, Zhiqiang Zhang, Ping Luo, Philip S. Yu, Yading Yue, Bin Wu. Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks. CIKM 2015: 453-462. [pdf] [code]
166. Bowei He, Zhiqiang Zhang, Jian Liu, Fuzhen Zhuang, Chuan Shi. Repeat Buyers Prediction after Sales Promotion for Tmall Platform. IJCAI 2015 workshop, 1st International Workshop on Social Influence Analysis. Champion in IJCAI 2015 Contest. [pdf]
167. Chuan Shi, Xiangnan Kong, Yue Huang, Philip S. Yu, Bin Wu. HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks. IEEE Transactions on Knowledge and Data Engineering, 26(10): 2479-2492, 2014. [pdf] [code & data]
168. Chuan Shi, Xiangnan Kong, Di Fu, Philip S. Yu. Multi-label Classification based on Multi-Objective Optimization. ACM Transaction on Intelligent Systems and Technology, 5(2), 35, 2014. [pdf]
169. Chuan Shi, Wang Ran, Yitong Li, Philip S. Yu, Bin Wu. Ranking-based Clustering on General Heterogeneous Information Networks by Network Projection. CIKM 2014: 699-708 [pdf]
170. Xiaofeng Meng, Chuan Shi, Yitong Li, Lei Zhang, Bin Wu. Relevance Measure in Large-Scale Heterogeneous Networks. APWeb 2014: 636-643 [pdf]
171. Gang Liu, Chuan Shi, Qing Chen, Bin Wu, and Jiayin Qi. A Two-Phase Model for Retweet Number Prediction. WAIM 2014: 781-792. <[pdf]
172. Yitong Li, Chuan Shi, Philip S. Yu, and Qing Chen. HRank: A Path Based Ranking Method in Heterogeneous Information Network. WAIM 2014: 553–565. [pdf]
173. Dongyu Wei, Xin Pan, Chuan Shi, and Yueguo Chen. A Novel Index Structure for Multi-key Search. WAIM 2014: 431–434. [pdf]
174. Chuan Shi, Philip S. Yu, Zhenyu Yan, Yue Huang, Bai Wang. Comparison and Selection of Objective Functions in Multi-Objective Community Detection. Computational Intelligence, 30(3): 562-582, 2014. [pdf]
175. Chuan Shi, Yanan Cai, Di Fu, Yuxiao Dong, Bin Wu. A Link Clustering based Overlapping Community Detection Algorithm. Data & Knowledge Engineering, 87: 394-404, 2013. [pdf] [code]
176. Jingfei Du, Jianyang Lai, Chuan Shi. Multi-objective Optimization for Overlapping Community Detection. ADMA 2013: 489-500. [pdf]
177. Ran Wang, Chuan Shi, Philip S. Yu, Bin Wu. Integrating Clustering and Ranking on Hybrid Heterogeneous Information Network. PAKDD 2013: 583-594. [pdf]
178. Chuan Shi, Zhenyu Yan, Yanan Cai, Bin Wu. Multi-objective community detection in complex networks. Applied Soft Computing, 12(2), 2012, 850-859. [pdf] [code]
179. Chuan Shi, Xiangnan Kong, Philip S. Yu. Multi-Objective Multi-Label Classification. SDM 2012: 355-366. [pdf]
180. Chuan Shi，Chong Zhou, Xiangnan Kong, Philip S. Yu, Gang Liu, Bai Wang. HeteRecom: A Semantic-based Recommendation System in Heterogeneous Networks. KDD 2012: 1552-1555. [pdf]
181. Chuan Shi, Xiangnan Kong, Philip S. Yu, Sihong Xie, Bin Wu. Relevance Search in Heterogeneous Networks. EDBT 2012: 180-191. [pdf]
182. Chuan Shi, Zhenyu Yan, Xin Pan, Yanan Cai, Bin Wu. A Posteriori Approach for Community Detection. Journal of Computer Science and Technology 26(5): 792-805 2011. [pdf]
183. Chuan Shi, Xiangnan Kong, Philip S. Yu, Bai Wang. Multi-label Ensemble Learning. ECML/PKDD (3) 2011: 223-239. [pdf]
184. Chuan Shi, Philip S. Yu, Yanan Cai, Zhenyu Yan, and Bin Wu. On Selection of Objective Functions in Multi-Objective Community Detection. CIKM 2011: 2301-2304. [pdf]
185. Chuan Shi, Zhenyu Yan, Xin Pan, Yanan Cai and Bin Wu. Multi-objective Decisionmaking in the Detection of Comprehensive Community Structures. CEC 2011: 1597-1604. [pdf]
186. Yanan Cai, Chuan Shi, Yuxiao Dong, Qing Ke, and Bin Wu, A Novel Genetic Algorithm for Overlapping Community Detection. ADMA 2011: 557-564. Best paper award [pdf]
187. Chuan Shi, Zhenyu Yan, Zhongzhi Shi, Lei Zhang, A Fast Multi-objective Evolutionary Algorithm based on a Tree Structure, Applied Soft Computing, 10(2), 2010, 468-480. [pdf]
188. Chuan Shi, Zhenyu Yan, Yi Wang, Yanan Cai, Bin Wu, A Genetic Algorithm for Detecting Communities in Large-scale Complex Networks, Advances in Complex Systems, 13(1), 2010, 3-17. [pdf]
189. Chuan Shi, Cha Zhong, Zhenyu Yan, et al., A Multi-Objective Optimization Approach for Community Detection in complex network, CEC 2010. [pdf]
190. Chuan Shi, Yanan Cai, Philip S. Yu, Zhenyu Yan, Bin Wu, A Comparison of Objective Functions in Network Community Detection, ICDM 2010 workshop：1234-1241. [pdf]
191. Chuan Shi, Jian Zhang, Liangliang Shi, Yanan Cai, Bin Wu, A Novel Algorithm for Hierarchical Community Structure Detection in Complex Networks, ADMA 2010: 557-564. [pdf]
192. 赵惠东,刘刚,石川,吴斌.基于转发传播过程的微博转发量预测[J]. 电子学报, 2016, 44(12): 2989-2996. [pdf]
193. 王锐,吴玲玲,石川,吴斌. 基于社团结构的链接预测和属性推断联合解决方法[J]. 电子学报, 2015, 44(9): 2062-2067. [pdf]
194. Chuan Shi, Zhenyu Yan, Zhongzhi Shi, Bai Wang, Dominance Tree and its Application in Evolutionary Multi-objective Optimization, Information Sciences, 179, 2009, 3540-3560. [pdf]
195. Chuan Shi, Dan Zhou, Bin Wu, Jian Liu, VisNetMiner: An Integration Tool for Visualization and Analysis of Networks, AMDA2009: 611-618. [pdf]
196. Chuan Shi, Yi Wang, Bin Wu, Cha Zhong, A New Genetic Algorithm for Community Detection, Complex 09, vol5, no1, 1298-1309. [pdf]
197. Shi Chuan, Shi ZhongZhi, Wang Maoguang, Online Hierarchical Reinforcement Learning Based on Path Matching. Journal of Computer Research and Development (Chinese), 45(9), 2008, 1470-1477. [pdf]
198. Lin Fen, Shi Chuan, Luo Jiewen, Shi ZhongZhi, Dual Reinforcement Learning Based on Bias Learning, Journal of Computer Research and Development (Chinese), 45(9), 2008, 1455-1462. [pdf]
199. Shi Chuan, Li qingyong, Shi zhongzhi, A Quick Multi-objective Evolutionary Algorithm Based on Dominating Tree, Journal of Software (Chinese), 18(3), 2007, 505-516. [pdf]
200. Chuan Shi, Zhongzhi Shi, Bin Wu, An Efficient Fitness Assignment Based on Dominating Tree, Workshops on ICDM 2007: 247-252. [pdf]
201. Chuan Shi, Rui Huang, Zhongzhi Shi, Automatic Discovery of Subgoals in Reinforcement Learning using Unique-Direction Value, ICCI2007: 480-486. [pdf]
202. Chuan Shi, Qingyong Li, Zhiyong Zhang, Zhongzhi Shi, An Improved Multiobjective Evolutionary Algorithm Based on Dominating Tree, PRICAI 2006: 691-700. [pdf]
203. Chuan Shi, Jiewen Luo, Fen Lin, A Multi-agent Negotiation Model Applied in Multi-objective Optimization, PRIMA2006: 305-314. [pdf]

University Teaching

• Introduction to Data Science
• Graph Machine Learning
• Formal Languages and Automata
• Computer Fundamentals and C Programming

Honors and Awards

• The First Prize of Science and Technology Progress of the Chinese Institute of Electronics (ranking first)
2023
• The Second Prize of Beijing Natural Science Award (ranking first)
2021
• The Second Prize of CCF Natural Science Award (ranking first)
2020
• The First Prize of Wu Wenjun Artificial Intelligence Technology Progress Award (ranking third)
2020
• The Second Prize of Scientific and Technological Progress of the Outstanding Achievement Award of Scientific Research in Colleges and Universities (ranking ninth)
2019
• The First Prize of Science and Technology Progress Award of State Grid Corporation (ranking fourth)
2019
• The best paper award in ADMA 2018
Dec. 2018
• The recipient of “the Pioneer of Teacher's Ethics” in Beijing
June 2018
• Zhou Jiongpan Excellent Young Teacher Award in BUPT
Sep. 2017
• Excellent Young AE of SCI journal "Frontiers of Computer Science"
May. 2017-2021
• Highly Cited Research in Data & Knowledge Engineering of ELSEVIER
Jan. 2017
• Excellence Award of CCF-Tencent Xi'niu'niao fund
Oct. 2015
• The champion in the IJCAI Contest 2015
Jul. 2015
• The recipient of the Youth Talent Plan in Beijing
Jul. 2013
• The best paper award in ADMA 2011
Dec. 2011