杨博  教授、博士生导师

    邮箱：ybo@jlu.edu.cn

    联系电话：0431-85166892

    办公地址：王湘浩楼B507

    教研室：知识科学与知识工程

基本信息

杨博，教授，博士生导师，国家级领军人才。现任伟德国际1946经理，软件公司经理，网络安全公司经理，符号计算与知识工程教育部重点实验室主任。2003年于伟德国际1946集团计算机公司获博士学位，曾赴香港浸会大学、英国华威大学、悉尼科技大学、乔治梅森大学等访问交流。主要研究领域：人工智能，知识工程。目前的研究方向：图机器学习与图挖掘，复杂系统学习，神经符号系统，智能推荐系统，图神经网络，图优化，遥感解译，人机融合系统等。

【招生信息】

杨博教授所在网络大数据研究科研团队现有教授、副教授、助理教授7人，在站博士后3名，博士生15名，硕士生22名。研究组常年招收博士研究生和硕士研究生，在读研究生在公司奖学金基础上可获额外科研津贴，2026年和2027年博士和硕士招生均有一定数量的报考名额，欢迎具有较强自驱力和浓厚科研兴趣的同学投递简历！（简历投递邮箱：ybo@jlu.edu.cn; cjx@jlu.edu.cn; xueyanliu@jlu.edu.cn）

招生专业：

-博士研究生

·伟德国际1946：计算机软件与理论（学术型），计算机技术（专业型）

-硕士研究生

·伟德国际1946：计算机软件与理论（学术型），计算机技术（专业型）

·软件公司：软件工程（学术型和专业型）

【毕业员工去向】

本组员工毕业后就职于阿里巴巴、腾讯、京东、百度、华为、用友、中国人民银行、中国银联总部、中国农行总部、国家电网、中国移动、一汽研发总院、上海期货等大型互联公司和国家重点企事业单位，香港大学、西安交通大学、伟德国际1946集团、东北师范大学、深圳信息职业技术公司等知名高等院校，或在香港科技大学、香港浸会大学、伟德国际1946集团、天津大学、哈尔滨工业大学等知名高校继续攻读博士学位。

最新消息

[2026/01]论文“Empowering LLMs for Structure-Based Drug Design via Exploration-Augmented Latent Inference.”被CCF A类会议 2026WWW录用！

[2026/01]论文“SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendation.”被CCF A类会议 2026WWW录用！

[2026/01]论文“Learning Evolving Preferences: A Federated Continual Framework for User-Centric Recommendation.”被CCF A类会议 2026WWW录用！

[2026/01]论文“Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach.”被CCF A类会议WWW 2026录用！

[2026/01]论文“Towards Faithful Reasoning in Remote Sensing: A Perceptually-Grounded GeoSpatial Chain-of-Thought for Vision-Language Models”被清华A类会议ICLR 2026录用！

[2026/01]于东然博士获得吉林省优秀博士学位论文奖！

[2025/11]论文“SkyMoE: A Vision-Language Foundation Model for Enhancing Geospatial Interpretation with Mixture of Experts”被CCF A 类会议AAAI 2026录用！

[2025/06]论文“Learning interpretable network dynamics via universal neural symbolic regression”被中科院一区期刊Nature Communications (NC)录用！

[2025/06]张春旭博士、黄思理博士获得伟德国际1946集团优秀博士学位论文奖！

[2025/05]论文“Code-Generated Graph Representations Using Multiple LLM Agents for Material Properties Prediction”被CCF A类会议ICML 2025录用！

[2025/05]论文“Generalizable Causal Reinforcement Learning for Out-of-Distribution Environments”被中科院一区期刊IEEE Transactions on Industrial Informatics (TII)录用！    

[2025/04]论文“Towards generalizable neural simulator: addressing distribution shift induced by environmental and temporal variations”被CCF A 类会议IJCAI 2025录用！ 

[2025/04]论文“ Distilling A Universal Expert from Clustered Federated Learning”被CCF A 类会议IJCAI 2025录用！

[2025/04]论文“State Feedback Enhanced Graph Differential Equations for Multivariate Time Series Forecasting”被CCF A 类会议IJCAI 2025录用！

[2025/04]论文“Hypercomplex Knowledge Graph-Aware Recommendation”被CCF A类会议SIGIR 2025录用！

[2025/01]论文“Dual Graph Denoising Model for Social Recommendation”被CCF A类会议WWW 2025录用！

[2024/12]李岸宸博士获得吉林省优秀博士学位论文奖！

[2024/11]论文“Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes”被CCF A类期刊SCIENCE CHINA Information Sciences (SCIS)录用！

[2024/11]论文“Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes”被中科院一区（CCF B类）期刊Neural Networks录用！

[2024/10]论文“Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling”被CCF A类会议NeurIPS 2024录用！

[2024/08]论文“Boosting Weak-to-Strong Agents in Multi-Agent Reinforcement Learning via Balanced PPO”被中科院一区（CCF B类）期刊IEEE Transactions on Neural Networks and Learning Systems（TNNLS）录用！

[2024/06]于东然博士、夏日婷博士获得伟德国际1946集团优秀博士学位论文奖！

[2024/06]论文“GPFedRec: Graph-Guided Personalization for Federated Recommendation”被CCF A类会议KDD 2024录用！

[2024/05]论文“Federated Adaptation for Foundation Model-based Recommendations”被CCF A类会议IJCAI 2024录用！

[2024/05]论文“Stochastic Neural Simulator for Generalizing Dynamical Systems across Environments”被CCF A类会议IJCAI 2024录用！

[2024/04]论文“In-Context Reinforcement Learning with Hierarchical Chain of Experience”被CCF A类会议ICML 2024录用！

[2024/03]论文“Structure- and Logic-aware Heterogeneous Graph Learning for Recommendation.”被CCF A类会议ICDE 2024录用！

[2024/02]裴红斌博士获得2023年度人工智能学会优博提名奖!

[2024/01]论文“Zero-shot Image Classification with Logic Adapter and Rule Prompt”被CCF A类会议WWW 2024录用！

[2024/01]论文“When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions”被CCF A类会议WWW 2024录用！

科研成果

近年来，研究组在贝叶斯优化、网络表示学习、图神经网络、社会化推荐系统、数据驱动的智能传染病防控、时空网络建模与挖掘等方面取得了多项创新性研究成果，主持或完成科技创新2030新一代人工智能重大项目，国家自然科学基金重点、面上、青年项目，华为校企联合等项目20多项，发表论文近200篇，其中在IEEE TPAMI、IEEE TKDE、IEEE TCYB、IEEE TNNLS、ACM TWEB、ACM TKDD、ML、JAAMAS、DKE、WWWJ、Information Sciences、AAAI、IJCAI、NeurIPS、ICLR、ACL、WWW、CIKM、ICDM、COLING、UbiComp等CCF A/B类期刊和会议上发表论文50余篇，国内一级学报论文20余篇，出版专著1部，译著1部，获国家发明专利10多项，软著20余项。项目组完成的项目组完成的“领域驱动的网络大数据分析理论与方法”获吉林省自然科学一等奖（2021），“大规模网络机器学习和数据挖掘方法”获吴文俊人工智能科学技术奖自然科学二等奖（2017），“复杂知识处理的基本方法研究”获吉林省自然科学二等奖（2014），“大数据和移动互联时代快速知识共享关键技术创新及应用”获中国商业联合会科学技术一等奖（2020）。

【各方向的相关论文】

图机器学习与图挖掘：

[1] Riting Xia, Chunxu Zhang, Yan Zhang, Xueyan Liu*, and Bo Yang*. A novel graph oversampling framework for node classification in class-imbalanced graphs.SCIENCE CHINA Information Sciences (SCIS). 2024, 67(6): 162101.   (CCF A)

[2] Riting Xia, Chunxu Zhang, Anchen Li, Xueyan Liu, and Bo Yang*. Attribute imputation autoencoders for attribute-missing graphs. Knowledge-Based Systems, 2024: 111583. (中科院1区)

[3] Xueyan Liu, Wenzhuo Song, Katarzyna Musial, Yang Li, Xuehua Zhao, Bo Yang*. Stochastic Block Models for Complex Network Analysis: A Survey. ACM Transactions on Knowledge Discovery from Data (TKDD), 2025. (CCF B)

[4] Haihong Zhao, Bo Yang*, Jiaxu Cui, Qianli Xing*, Jiaxing Shen, Fujin Zhu, and Jianong Cao. Effective Fault Scenario Identification for Communication Networks via Knowledge-Enhanced Graph Neural Networks. IEEE Transactions on Mobile Computing (TMC), 2023. (CCF A)

[5] Bo Yang, Xueyan Liu, Yang Li, Xuehua Zhao. Stochastic blockmodeling and variational Bayes learning for signed network analysis, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2017, 29(9): 2026-2039. （CCF A）

[6] Bo Yang, Xuehua Zhao. On the scalable learning of stochastic blockmodel. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI’15), Jan 25-30, 2015:360-366. (CCF A)

[7] Bo Yang, Xuehua Zhao, Xueyan Liu. Bayesian approach to modeling and detecting communities in signed network. The 29th AAAI Conference on Artificial Intelligence (AAAI’15), Jan 25-30, 2015: 1952-1958. (CCF A)

[8] Bo Yang, Jiming Liu, Jianfeng Feng. On the spectral characterization and scalable mining of network communities. IEEE Transactions on Knowledge and Data Engineering (TKDE),2012,24(2):326-337. (CCF A)

[9] Bo Yang, William K. Cheung, Jiming Liu. Community mining from signed social networks. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2007, 19(10): 1333-1348. (CCF A) 被引507次

[10] Riting Xia, Yan Zhang, Xueyan Liu*, Bo Yang*. A survey of sum-product networks structural learning. Neural Networks, 2023, 164: 645-666. (CCF B)

[11] Xueyan Liu, Bo Yang*, Hechang Chen, Katarzyna Musial, Hongxu Chen, Yang Li, Wangli Zuo. A Scalable Redefined Stochastic Blockmodel, ACM Transactions on Knowledge Discovery and Data Mining (TKDD), 2021, 15(3): 1-28. (CCF B)

[12] Xueyan Liu, Bo Yang*, Wenzhuo Song, Katarzyna Musial, Wanli Zuo, Hongxu Chen, Hongzhi Yin. A block-based generative model for attributed network embedding. World Wide Web (WWWJ), 2021, 24(5): 1439-1464. (CCF B)

[13] Bo Yang, Jiming Liu, Da-you Liu. Characterizing and extracting multiplex patterns in complex networks. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics (TCYB), 2012, 42(2):469-481. (CCF B)

[14] Bo Yang, Di Jin, Jiming Liu, Dayou Liu. Hierarchical community detection with applications to real-world network analysis. Data & Knowledge Engineering (DKE), 2013, 83: 20-38. (CCF B)

[15] Xueyan Liu, Wenzuo Song, Katarzyna Musial, Xuehua Zhao, Wanli Zuo, Bo Yang*, Semi-supervised stochastic blockmodel for structure analysis of signed networks, Knowledge-Based Systems (KBS), 2020, 195: 105714. (中科院1区)

[16] Bo Yang, Hechang Chen, Xuehua Zhaoa, Masato Naka, Jing Huang. On characterizing and computing the diversity of hyperlinks for anti-spamming page ranking. Knowledge-Based Systems (KBS), 2015, 77: 56-67. (中科院1区)

[17] Bo Yang, William K. Cheung, Jiming Liu. Community mining from signed social networks. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2007, 19(10): 1333-1348. (CCF A)

[18] Yang Li, Wenzhuo Song, Bo Yang*. Stochastic variational inference-based parallel and online supervise topic mode. Journal of Computer Science and Technology (JCST), 2018, 33(5): 1007-1022. (CCF B)

[19] 赵学华, 杨博*, 陈贺昌. 一种高效的随机块模型学习算法.软件学报, 2016, 27(9): 2248-2264.

[20] 杨博, 陈贺昌, 朱冠宇, 赵学华. 基于超链接多样性分析的新型网页排名算法. 计算机学报, 2014, 34(4): 833-847.

[21] 刘大有, 金弟, 何东晓, 杨建宁, 黄晶, 杨博*. 复杂网络社区挖掘综述. 计算机研究与发展, 2013, 50(10): 2140-2154

[22] 杨博, 刘杰, 刘大有. 基于随机网络集成模型的广义网络社区挖掘算法.自动化学报,2012,38(5): 812-822.

[23] 杨博,刘大有, 刘际明,金弟,马海宾.复杂网络聚类方法. 软件学报, 2009, 20(1): 54-66. 被引638次

复杂系统学习/流行病防控：

[24] Jiao Hu, Jiaxu Cui*, Bo Yang*. Learning interpretable network dynamics via universal neural symbolic regression[J]. Nature Communications, 2025, 16(1): 6226.( SCI 1区)

[25] Jiaqi Liu, Jiaxu Cui*, Shiang Sun, Yizhu Zhao,  Bo Yang*. Towards Generalizable Neural Simulators: Addressing Distribution Shifts Induced by Environmental and Temporal Variations. International Joint Conference on Artificial Intelligence (IJCAI'25), 2025.

[26] Jiaxu Cui, Qipeng Wang, Yiming Zhao, Bingyi Sun, Pengfei Wang, Bo Yang*. State Feedback Enhanced Graph Differential Equations for Multivariate Time Series Forecasting. International Joint Conference on Artificial Intelligence (IJCAI'25), 2025. (CCF-A类会议）

[27] Sili Huang, Jifeng Hu, Hechang Chen∗, Peng Cui, Haiyin Piao, Lichao Sun, Bo Yang∗. Generalizable Causal Reinforcement Learning for Out-of-Distribution Environments [J]. IEEE Transactions on Industrial Informatics (TII), 2025. (中科院I一区期刊)

[28] Jiaxu Cui, Qipeng Wang, Bingyi Sun, Jiming Liu, Bo Yang*. Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes. SCIENCE CHINA Information Sciences (SCIS), 2024. (CCF A)

[29] Sili Huang, Hechang Chen*, Haiyin Piao, Zhixiao Sun, Yi Chang, Lichao Sun, Bo Yang*. Boosting Weak-to-Strong Agents in Multi-Agent Reinforcement Learning via Balanced PPO. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2024. (CCF B)

[30] Sili Huang, Jifeng Hu, Zhejian Yang, Liwei Yang, Tao Luo, Hechang Chen*, Lichao Sun, Bo Yang*. Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling. Neural Information Processing Systems (NeurIPS'24), 2024. (CCF-A类会议）

[31] Jiaqi Liu, Jiaxu Cui*, Jiayi Yang, Bo Yang*. Stochastic Neural Simulator for Generalizing Dynamical Systems across Environments. The 33nd International Joint Conference on Artificial Intelligence (IJCAI'24), Aug 03-09, 2024. (CCF A)

[32] Sili Huang, Jifeng Hu, Hechang Chen, Lichao Sun, Bo Yang*. In-Context Reinforcement Learning with Hierarchical Chain of Experience. The Forty-first International Conference on Machine Learning (ICML'24), Jul 21-27, 2024. （CCF A）

[33] Sili Huang, Yanchao Sun, Jifeng Hu, Siyuan Guo, Hechang Chen*, Yi Chang*, Lichao Sun, Bo Yang*. Learning Generalizable Agents via Saliency-guided Features Decorrelation. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPs’23). Dec 10-16, 2023. (CCF A)

[34] Bing Liu, Wei Luo, Gang Li, Jing Huang, Bo Yang. Do we need an encoder-decoder to model dynamical systems on networks? The 32nd International Joint Conference on Artificial Intelligence (IJCAI’23), Aug 8-19, 2023

[35] Hongbin Pei, Bo Yang*, Jiming Liu, Kevin Chang. Active Surveillance via Group Sparse Bayesian Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),2022, 44(3): 1133-1148. (CCF A) ESI 热点论文、高被引论文

[36] Bo Yang, Hongbin Pei, Hechang Chen, Jiming Liu, Shang Xia. Characterizing and discovering spatiotemporal social contact patterns for healthcare. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017, 39(8), 1532-1546. （CCF A）

[37] Hongbin Pei, Bo Yang*, Jiming Liu, Lei Dong. Group sparse Bayesian learning for actively surveillance on epidemic dynamics. In Proceedings of 32nd AAAI Conference on Artificial Intelligence (AAAI’18), Feb 2-7, 2018. （CCF A）

[38] Yuan Bai, Bo Yang*, Zhanwei Du, Lauren Ancel Meyers. Location based surveillance for early detection of contagious outbreaks. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’15), Sept 7-11, 2015: 77-80. （CCF A）

[39] Bo Yang, Hua Guo, Yi Yang, Benyun Shi, Xiaonong Zhou, Jiming Liu. Modeling and mining spatiotemporal patterns of Infection risk from heterogeneous data for active surveillance planning. The 28th AAAI Conference on Artificial Intelligence (AAAI’14), Jul 27-31, 2014: 493-499. (CCF A)

[40] Dayou Liu, Bo Yang*, Shang Gao, Yungang Zhu, Yong Lai. Intelligent CPSS and its application to health care computing, Science China (information sciences), 2016, 59(5): 050103:1-3. (CCF B)

[41] Bo Yang, Hongbin Pei, Hechang Chen, Jiming Liu, Shang Xia. Modeling and mining spatiotemporal social contact of metapopulation from heterogeneous data. IEEE 14th International Conference on Data Mining (ICDM ’14), Dec 14-17, 2014: 630- 639. (CCF B)

[42] 杨博,刘际明,杨建宁,白媛,刘大有.基于自治计算的流行病传播网络建模与推断.软件学报, 2012, 23(11): 2955-2970.

图神经网络、图优化和神经符号系统：

[43] Xuanning Hu, Anchen Li, Qianli Xing*, Jinglong Ji, Hao Tuo and Bo Yang*. Empowering LLMs for Structure-Based Drug Design via Exploration-Augmented Latent Inference. The Web Conference 2026 (WWW), 2026 (CCF-A)

[44] Jiaqi Liu, Lang Sun, Ronghao Fu*, Bo Yang*. Towards Faithful Reasoning in Remote Sensing: A Perceptually-Grounded GeoSpatial Chain-of-Thought for Vision-Language Models. International Conference on Learning Representations (ICLR), 2026. (清华A类论文)

[45] Jiaqi Liu, Ronghao Fu*, Lang Sun, Haoran Liu, Xiao Yang, Weipeng Zhang, Xu Na, Zhuoran Duan, Bo Yang*. SkyMoE: A Vision-Language Foundation Model for Enhancing Geospatial Interpretation with Mixture of Experts. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI),  2026. (CCF A)

[46] Jiao Huang, Qianli Xing, Jinglong Ji, Bo Yang. Reinforcement Learning with Formation Energy Feedback for Material Diffusion Models[J]. Neural Networks (NN), 2026.

[47] Jiao Huang, Qianli Xing*, Jinglong Ji, Bo Yang*. Code-Generated Graph Representations Using Multiple LLM Agents for Material Properties Prediction. International Conference on Machine Learning (ICML), 2025.（CCF-A会）

[48] Jiao Huang, Qianli Xing*, Jinglong Ji, Bo Yang*. PerCNet: Periodic complete representation for crystal graphs. Neural Networks (NN), 2025. (CCF B)

[49] Dongran Yu, Xueyan Liu*, Bo Yang*. Zero-shot Image Classification with Logic Adapter and Rule Prompt. In Proceedings of the ACM Web Conference (WWW’24), May 13–17, 2024. (CCF A)

[50] Chunxu Zhang, Ximing Li, Hongbin Pei, Zijian Zhang, Bing Liu, Bo Yang*. LaenNet: Learning robust GCNs by propagating labels. Neural Networks, 2023, 168: 652-664. (CCF B)

[51] Dongran Yu, Bo Yang*, Dayou Liu, Hui Wang, Shirui Pan. A survey on neural-symbolic learning systems. Neural Networks,2023, 166: 105-126. (CCF B)

[52] Riting Xia, Yan Zhang, Chunxu Zhang, Xueyan Liu*, Bo Yang*. Multi-head Variational Graph Autoencoder Constrained by Sum-product Networks. In Proceedings of the ACM Web Conference (WWW’23), Apr 30-May 4, Austin, TX, USA, 2023. (CCF A)

[53] Dongran Yu, Bo Yang*, Qianhao Wei, Anchen Li, Shirui Pan. A Probabilistic Graphical Model Based on Neural-Symbolic Reasoning for Visual Relationship Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 10609-10618. (CCF A)

[54] Jiaxu Cui, Bo Yang*, Bingyi Sun, Jiming Liu. Cost-aware Graph Generation: A Deep Bayesian Optimization Approach. The 35th AAAI Conference on Artificial Intelligence (AAAI’21), Feb 2-9, 2021,7142-7150. (CCF A)

[55] Hongbin Pei, Bingzhe Wei, Kevin Chang, Chunxu Zhang, Bo Yang*. Curvature Regularization to Prevent Distortion in Graph Embedding. The 34th International Conference on Neural Information Processing Systems (NeurPIS’20), Dec 6-12, 2020, 1-12. (CCF A)

[56] Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, Bo Yang*. Geom-GCN: geometric graph convolutional networks. In Proceedings of the 8th International Conference on Learning Representations (ICLR’20), Apr 26-30, 2020, 1-12. (清华A类论文). 被引1253次.

[57] Jiaxu Cui, Bo Yang*, Xia Hu. Deep Bayesian optimization on attributed graphs. In Proceedings of 33rd AAAI Conference on Artificial Intelligence (AAAI’19), Jan 27-Feb 1, 2019, 1377-1384. (CCF A)

[58] Jiaxu Cui, Bo Yang*, Bingyi Sun, Xia Hu, Jiming Liu. Scalable and Parallel Deep Bayesian Optimization on Attributed Graphs, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022, 33(1): 103-116. (CCF B)

[59] Jiaxu Cui, Qi Tan, Chunxu Zhang, Bo Yang*. A Novel Framework of Graph Bayesian Optimization and Its Applications to Real-World Network Analysis. Expert System with Applications, 2021, 170: 114524. (中科院1区)

[60] 崔佳旭, 杨博*. 贝叶斯优化方法和应用综述. 软件学报, 2018, 29(10)：3068-3090

[61] 杨博，张钰雪晴，彭羿达，张春旭，黄晶. 一种协同过滤式零次学习方法，软件学报，2021,32(9):2801-2815

智能推荐系统：

[62] Chunxu Zhang, Shanqiang Huang, Zijian Zhang*, Jiahong Liu, Linsong Yu, Ruiqi Wan, Bo Yang*, Irwin King. SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendation. The Web Conference 2026 (WWW), 2026 (CCF-A)

[63] Chunxu Zhang, Zhiheng Xue (co-first author), Guodong Long, Weipeng Zhang, Bo Yang*. Learning Evolving Preferences: A Federated Continual Framework for User-Centric Recommendation. The Web Conference 2026 (WWW), 2026 (CCF-A)

[64] Chunxu Zhang, Weipeng Zhang (co-first author), Guodong Long, Zhiheng Xue, Riting Xia, Bo Yang*. Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach. The Web Conference 2026 (WWW), 2026 (CCF-A)

[65] Anchen Li, Bo Yang*, Huan Huo, Farookh Hussain, Guandong Xu. Hypercomplex Knowledge Graph-Aware Recommendation. International SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2025. (CCF A)

[66] Anchen Li, Bo Yang*. Dual Graph Denoising Model for Social Recommendation.  Proceedings of the Web Conference (WWW), 2025. (CCF A)

[67] Zeqi Leng, Chunxu Zhang*, Guodong Long, Riting Xia, Bo Yang*. Distilling A Universal Expert from Clustered Federated Learning. International Joint Conference on Artificial Intelligence (IJCAI'25), 2025. (CCF-A类会议)

[68]  Chunxu Zhang, Guodong Long, Tianyi Zhou, Zijian Zhang, Peng Yan, Bo Yang*. GPFedRec: Graph-Guided Personalization for Federated Recommendation. The 30th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'24), Aug 25-29, 2024.  (CCF A)

[69]  Chunxu Zhang, Guodong Long, Hongkuan Guo, Xiao Fang, Yang Song, Zhaojie Liu, Guorui Zhou, Zijian Zhang, Yang Liu*, Bo Yang*. Federated Adaptation for Foundation Model-based Recommendations. The 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), Aug 3-9, 2024. （CCF A）

[70] Anchen Li, Bo Yang∗, Huan Huo, Farookh Khadeer Hussain, Guandong Xu. Structure- and Logic-aware Heterogeneous Graph Learning for Recommendation. IEEE International Conference on Data Engineering (ICDE). May 13-17, 2024. (CCF A)

[71] Chunxu Zhang, Guodong Long, Tianyi Zhou, Zijian Zhang, Peng Yan, Bo Yang*. When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions. In Proceedings of the ACM Web Conference (WWW’24), May 13-17, 2024. (CCF A)

[72] Anchen Li, Bo Yang*, Huan Huo, Hongxu Chen, Guandong Xu, and Zhen Wang. Hyperbolic Neural Collaborative Recommender. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023, 35(9): 9114-9127. (CCF A)

[73] Bo Yang, Yu Lei, Jiming Liu, Wenjie Li. Social collaborative filtering by trust. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017, 39(8), 1633-1647. （CCF A）ESI 高被引论文. 被引816次.

[74] Chunxu Zhang, Guodong Long, Tianyi Zhou, Peng Yan, Zijian Zhang, Chengqi Zhang, Bo Yang*. Dual Personalization on Federated Recommendation. The 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), Aug 19-25, 2023. （CCF A）

[75] Anchen Li, Bo Yang*, Huan Huo, Farookh Hussain. Hypercomplex Graph Collaborative Filtering. In Proceedings of the ACM Web Conference (WWW’22), Apr 25-29, 2022. (CCF A)

[76] Bo Yang, Yu Lei, Dayou Liu, Jiming Liu. Social collaborative filtering by trust. The 23rd International Joint Conference on Artificial Intelligence (IJCAI’13), Aug 3-9, 2013: 2747-2753. (CCF A)

[77] Anchen Li, Bo Yang*, Farookh Khadeer Hussain, Huan Huo. HSR: Hyperbolic Social Recommender, Information Sciences, 2022, 585: 275-288. (CCF B)

[78] Anchen Li, Bo Yang*, Huan Huo, Farookh Khadeer Hussain. Leveraging Implicit Relations for Recommender Systems, Information Sciences, 2021, 579: 55-71. (CCF B)

[79] Anchen Li, Bo Yang*. GSIRec: Learning with graph side information for recommendation, World Wide Web (WWWJ), 2021, 24(5): 1411-1437. (CCF-B)

[80] Yuyao Liu, Bo Yang, Hongbin Pei, Huang Jing*. Neural Explainable Recommender Model Based on Attributes and Reviews. Journal of Computer Science and Technology (JCST), 2020, 35(6): 1446-1460. (CCF B)

【在研的科研项目】

[1]复杂动态系统智能理论与方法研究，科技创新2030—“新一代人工智能”重大项目

[2]领域驱动的新型属性图优化理论、方法及应用研究，国家自然科学基金重点项目

[3]融合深度学习和贝叶斯优化的网络优化理论与方法，国家自然科学基金面上项目

[4]面向大规模网络分析的贝叶斯随机块模型与算法研究，国家自然科学基金面上项目

[5]基于网络多元结构的虚假信息检测方法研究，国家自然科学基金青年科学基金项目

[6]复杂系统动力学机制的灰盒学习方法，国家自然科学基金青年科学基金项目

员工培养

博士毕业生：

2021级 黄思理，毕业论文：《强化学习的策略迁移关键技术研究》；毕业去向：中央民族大学，讲师

2019级 张春旭，毕业论文：《基于深度学习的联邦推荐系统研究》；毕业去向：伟德国际1946集团，博士后

2019级 夏日婷，毕业论文：《面向属性网络分析的鲁棒图神经网络研究》；毕业去向：内蒙古大学，研究员（骏马计划B岗）

2019级 于东然，毕业论文：《基于统计关系学习的神经符号系统研究》；获吉林省优秀博士学位论文奖，毕业去向： 广西师范大学，博士后

2020级 李岸宸，毕业论文：《基于图表示学习的推荐模型研究》；获国家奖学金、获吉林省优秀博士学位论文奖

2017级 左祥麟，毕业论文：《面向异构信息的多视角推荐模型研究》；获国家奖学金；毕业去向：伟德国际1946集团，博士后

2017级 刘学艳，毕业论文：《面向复杂网络分析的随机块模型研究》；毕业去向：伟德国际1946集团，准聘副教授

2016级 崔佳旭，毕业论文：《贝叶斯图优化：关注成本的网络优化理论与方法》；获吉林省优秀博士学位论文奖；毕业去向：伟德国际1946集团，准聘副教授

2015级 裴红斌，毕业论文：《复杂网络的几何与稀疏表示学习方法研究》；获吉林省优秀博士学位论文、人工智能学会优博提名奖，毕业去向：国家博新计划，西安交通大学，助理教授

2014级 陈贺昌，毕业论文：《面向传染病主动监控的异构数据挖掘方法研究》；获国家奖学金，毕业去向：伟德国际1946集团，人工智能公司副教授

2013级 李洋，毕业论文：《面向两种概率图模型的高效学习方法研究》；毕业去向：空军航空大学，教授

2012级 白媛，毕业论文：《面向传染病领域的人类接触模式研究》；毕业去向：香港大学，博士后

2011级 赵学华，毕业论文：《统计网络模型若干关键问题研究》；获伟德国际1946集团优秀博士学位论文，毕业去向：深圳信息职业技术公司，副教授

硕士毕业生：

截止2024年9月，研究组已毕业硕士生70余名，其中就职于阿里巴巴、腾讯、百度、华为、字节跳动、滴滴等科技公司的同学30余名；就职于一汽、国家电网、中国移动、银联、银行、医院、高校等国企事业单位的同学20余名；继续读博深造的同学近20名。