j9九游国际站(中国)-九游会官网j9

赵兴旺，博士，教授，博士生导师，主要研究方向为数据挖掘与机器学习。近年来，先后主持国家自然科学基金项目2项、山西省自然科学研究项目2项，山西省教学改革项目2项，作为主要成员参与科技创新2030-“新一代人工智能”重大项目1项，国家自然科学基金重点项目2项；在《Pattern Recognition》、《Information Sciences》、《Knowledge-Based Systems》、《软件学报》、《计算机研究与发展》和NeurIPS、CVPR、AAAI、IJCAI、ICMR等国际国内重要学术刊物和学术会议上发表学术论文40余篇；获国家发明专利2项；作为主要完成人获山西省科技进步二等奖和山西省教学成果奖（高等教育研究生）特等奖各1项；获2019年度ACM 太原分会学术新星奖；完成的博士学位论文《大规模复杂数据聚类算法研究》获2019年度山西省优秀博士学位论文奖；指导的研究生荣获山西省优秀硕士学位论文、研究生国家奖学金等重要荣誉及奖励。

[1] Jun Wang, Fuyuan Cao*, Zhixin Xue, Xingwang Zhao, Jiye Liang. CMoB: Modality valuation via causal effect for balanced multimodal learning. The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS), San Diego, CA, Dec 2nd-7th, 2025

[2] Yan Wang, Fuyuan Cao*, Xingwang Zhao. Conflict-aware adaptive cross-reconstruction for multimodal sentiment analysis. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2026

[3] Shunyong Li, Mengjiao Zheng, Kun Liu, Xingwang Zhao*. Multi-view clustering algorithm based on bipartite graph learning and high-order information fusion, Information Sciences, 2026, 742: 123297

[4] Wentao Li, Xingwang Zhao*, Zhiqiang Li, Shiyi Li, Jinghan Yang. Multi-view graph contrastive clustering via consensus constraint, Neurocomputing, 2026, 676: 133069

[5] Zhiqiang Wang, Jiayi Pan, Xingwang Zhao*, Jianqing Liang, Chenjiao Feng, Kaixuan Yao. Counterfactual task-augmented meta-learning for cold-start sequential recommendation. The 39th Annual AAAI Conference on Artificial Intelligence (AAAI), Philadelphia, Pennsylvania, USA, 2025-2-25 to 2025-3-4

[6] Wentao Li, Xingwang Zhao*, Zhiqiang Wang. Multi-layer graph clustering with lightweight contrastive learning. The 15th ACM International Conference on Multimedia Retrieval (ICMR), 2025

[7] Zhiqiang Wang, Baijing Hu, Xingwang Zhao*, Yajun Lin, Yuqi Wang. Dynamic graph neural network with motif reconstruction. International Joint Conference on Neural Networks (IJCNN 2025), 2025

[8] Zhiqiang Li, Jie Wang, Jianqing Liang*, Junbiao Cui, Xingwang Zhao*, Jiye Liang. Uncertainty-guided graph contrastive learning from a unified perspective, The 34th International Joint Conference on Artificial Intelligence (IJCAI-25), 2025

[9] Xingwang Zhao, Shujun Wang, Xiaolin Liu, Jiye Liang*. Multi-view clustering via dynamic unified bipartite graph learning, Pattern Recognition, 2024, 156: 110715

[10] Xingwang Zhao, Jiye Liang*, Jie Wang, A community detection algorithm based on graph compression for large-scale social networks. Information Sciences, 2021, 551: 358-372

[11] Xingwang Zhao, Jiye Liang*, Chuangyin Dang. Clustering ensemble selection for categorical data based on internal validity indices, Pattern Recognition, 2017, 69: 150-168

[12] Xingwang Zhao, Jiye Liang*, Chuangyin Dang, A stratified sampling based clustering algorithm for large-scale data, Knowledge-Based Systems, 2019, 163: 416-428

[13] Xingwang Zhao, Fuyuan Cao, Jiye Liang*. A sequential ensemble clusterings generation algorithm for mixed data, Applied Mathematics and Computation, 2018, 335: 264-277

[14] Jiye Liang*, Xingwang Zhao, Deyu Li, Fuyuan Cao, Chuangyin Dang, Determining the number of clusters using information entropy for mixed data, Pattern Recognition, 2012, 45(6): 2251-2265

[15] Xingwang Zhao, Jiye Liang*, Fuyuan Cao. A simple and effective outlier detection algorithm for categorical data. International Journal of Machine Learning and Cybernetics. 2014, 5(3): 469-477

[16] Jie Wang, Jiye Liang*, Wenping Zheng, Xingwang Zhao, Junfang Mu. Protein complex detection algorithm based on multiple topological characteristics in PPI networks. Information Sciences. 2019, 489: 78-92

[17] Fuyuan Cao, Joshua Zhexue Huang, Jiye Liang, Xingwang Zhao, Yinfeng Meng. An algorithm for clustering categorical data with set-valued features, IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(10): 4593-4606

[18] Fuyuan Cao, Jiye Liang, Liang Bai, Xingwang Zhao, Chuangyin Dang, A framework for clustering categorical time-evolving data, IEEE Transactions on Fuzzy Systems, 2010, 18(5): 872-882

[19] Yunsheng Song, Jiye Liang*, Jing Lu, Xingwang Zhao. An efficient instance selection algorithm for k nearest neighbor regression, Neurocomputing, 2017, 251: 26-34

[20] Fuyuan Cao, Jiye Liang*, Deyu Li, Xingwang Zhao. A weighting k-Modes algorithm for subspace clustering of categorical data, Neurocomputing, 2013, 108: 23-30

[21] 赵兴旺, 王淑君, 刘晓琳, 梁吉业*. 基于二部图的联合谱嵌入多视图聚类算法. 软件学报, 2024, 35(9): 4408-4424

[22] 赵兴旺, 侯哲栋, 姚凯旋, 梁吉业. 基于注意力机制的两阶段融合多视图图聚类算法. 清华大学学报 (自然科学版), 2024, 64(1): 1-12

[23] 赵兴旺, 张珧溥, 梁吉业*. 基于2阶段集成的多层网络社区发现算法. 计算机研究与发展, 2023, 60(12): 2832-2843

[24] 刘晓琳, 白亮, 赵兴旺, 梁吉业. 基于多阶近邻扩散融合的不完整多视图聚类算法. 软件学报, 2022, 33(4): 1354-1372

[25] 赵兴旺, 梁吉业. 一种基于信息熵的混合数据属性加权聚类算法. 计算机研究与发展. 2016, 53(5): 1018-1028

[26] 张凯涵, 梁吉业, 赵兴旺, 王智强. 一种基于社区专家信息的协同过滤推荐算法. 计算机研究与发展, 2018, 55(5): 968-976

[27] 史倩玉, 梁吉业, 赵兴旺. 一种不完备混合数据集成聚类算法, 计算机研究与发展, 2016, 53(9): 1979-1989

[28] 李顺勇, 原志英, 赵兴旺. 基于Transformer融合的深度对比多视图聚类, 模式识别与人工智能, 2025, 38(12): 1057-1074

[29] 郭兰杰, 梁吉业, 赵兴旺. 融合社交网络信息的协同过滤推荐算法, 模式识别与人工智能, 2016, 29(3): 281-288

[30] 赵兴旺, 侯哲栋, 姚凯旋, 梁吉业. 基于注意力机制的两阶段融合多视图图聚类, 清华大学学报 (自然科学版). 2024, 64(01): 1-12

[1] 可解释性多视图聚类算法研究, 国家自然科学基金面上项目, 2021.01-2024.12, 主持

[2] 多粒度视角下大规模数据聚类算法研究, 国家自然科学基金青年项目, 2017.01-2019.12, 主持

[3] 认知计算基础理论与方法研究, 科技创新2030-“新一代人工智能”重大项目, 2020/10-2014/10, 参与

[4] 复杂多模态数据的聚类分析及云平台构建, 国家自然科学联合基金重点项目, 2025/01-2028/12, 参与

[5] 面向大数据的粒计算理论与方法, 国家自然科学基金重点项目, 2015.01-2019.12, 参与

[6] 面向关联关系数据的概念学习方法研究, 国家自然科学基金面上项目, 2016.01-2019.12, 参与

[7] 面向广义多视图数据的聚类算法研究, 国家自然科学基金面上项目, 2020.01-2023.12, 参与

[8] 面向大数据的半监督粗糙特征选择高效算法研究, 国家自然科学基金青年项目, 2015.01-2017.12, 参与

[9] 面向多源大数据的鲁棒聚类模型与算法研究, 国家自然科学基金青年项目, 2016.01-2018.12, 参与