刘斌
- 副教授 硕士生导师
- 职务 副教授 硕士生导师
Email:liubin@swufe.edu.cn
四川省成都市温江区柳台大道555号
研究方向
机器学习，自然语言处理，数据挖掘.
教育经历
2013.9--2017.12 电子科大学 博士
2016.6—2017.6 英属哥伦比亚大学 访问学生
2008.9--2011.6 电子科大学 硕士
2004.8--2008.7 辽宁工业大学 本科
工作经历
2018年--至今 西南财经大学
2018.9—2020.9 香港大学（博士后）
研究方向
图表示学习算法：图表示学习在关系数据分析上的算法、理论及应用研究。
应用场景
图表示学习在产业链上的应用研究；
图表示学习在ASD诊断以及H&E 肿瘤切片数据分析上的应用；
学术成果以及研究项目详见GitHub。
2013.9--2017.12 电子科大学 博士
2016.6—2017.6 英属哥伦比亚大学 访问学生
2008.9--2011.6 电子科大学 硕士
2004.8--2008.7 辽宁工业大学 本科
工作经历
2018年--至今 西南财经大学
2018.9—2020.9 香港大学（博士后）
研究方向
图表示学习算法：图表示学习在关系数据分析上的算法、理论及应用研究。
应用场景
图表示学习在产业链上的应用研究；
图表示学习在ASD诊断以及H&E 肿瘤切片数据分析上的应用；
学术成果以及研究项目详见GitHub。
Preprints
1. Chunhong Ye, Bin Liu*. Exploring Asset Pricing Through Industrial Chain Dynamics: Insights from Graph Machine Learning. SSRN Electronic Journal (2025)
2. Lu Wei, Yi Huang, Guosheng Yin, Fode Zhang, Manxue Zhang, Bin Liu*. Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph. arXiv preprint arXiv:2410.07138 (2024).
3. Ling Xiang, Quan Hu, Xiang Zhang, Wei Lan, Bin Liu*. Graph Neural Poisson Models for Supply Chain Relationship Forecasting. arXiv preprint arXiv:2508.12044 (2025).
4. Yuedi Zhang, Zhixiang Xia, Guosheng Yin, Bin Liu*. Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images arXiv preprint arXiv:2509.11034 (2025)
Conference Papers
1. Bin Liu, Yu Liu, Zhiqian Li, Jianghe Xiao, Huazhen Lin, Guosheng Yin. Automatic radiotherapy treatment planning with deep functional reinforcement learning. KDD, 2025, Toronto, ON, Canada, pp. 2426-2435.
2. Zhizhong Tan, Min Hu, Bin Liu*, Guosheng Yin. Futures Quantitative Investment with Heterogeneous Continual Graph Neural Networks. ICDM, 2024, Abu Dhabi, UAE, pp. 851-856
3. Jinjin Li, Bin Liu*, Chengyan Liu, Hongli Zhang. Predicting Housing Transaction with Common Covariance GNNs. IJCAI, 2024, Jeju, Korea, pp. 7323-7330.
4. Jiujun He, Bin Liu, Guosheng Yin. Enhancing Semi-supervised Domain Adaptation via Effective Target Labeling. AAAI, 2024, Vancouver, Canada, pp. 12385-12393.
5. Bin Liu, Jiujun He, Ziyuan Li, Xiaoyang Huang, Xiang Zhang, Guosheng Yin. Interpret ESG Rating’s Impact on the Industrial Chain Using Graph Neural Networks. IJCAI, 2023, Macao, China, pp. 6076-6084.
6. Lu Wei, Bin Liu*, Jiujun He, Manxue Zhang, Yi Huang. Autistic Spectrum Disorders Diagnose with Graph Neural Networks. ACM Multimedia, 2023, Ottawa, Canada, pp. 8819–8827.
7. Zhuo Tan, Bin Liu*, Guosheng Yin. Asymmetric Self-Supervised Graph Neural Networks. IEEE International Conference on Big Data, Osaka, Japan, 2022, pp. 1369-1376.
8. Jiujun He, Bin Liu, Xuan Yang. Non-local Patch Mixup for Unsupervised Domain Adaptation. IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, 2022, pp. 969-974.
9. Bin Liu, Wang Liang, Yin Guosheng. Learning distributed sentence vectors with bi-directional 3D convolutions. The 28th International Conference on Computational Linguistics(COLING), Barcelona, Spain, 2020, pp. 6820–6830.
10. Bin Liu, Xiaoxue Gao, Mengshuang He, Lin Liu, Guosheng Yin. A Fast Online COVID-19 Diagnostic System with Chest CT Scans. The 26TH ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Health Day), 2020, Virtual Conference. The online diagnosis system:https://www.covidct.cn/
11. Bin Liu, Guosheng Yin. Chinese document classification with bi-directional convolutional language model. The 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Xi’an, China, 2020, pp. 1785–1788.
12. Bin Liu, Zenglin Xu, Bo Dai, Haoli Bai, Xianghong Fang, Yazhou Ren, Shandian Zhe. Learning from semantically dependent multi-tasks. International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 2017, pp. 3498-3505.
13. Haoli Bai, Zenglin Xu, Bin Liu, Yingming Li. Hierarchical probabilistic matrix factorization with network topology for multi-relational social network. Proceedings of The 8th Asian Conference on Machine Learning, PMLR 63:270-285, 2016.
14. Bin Liu, Chao Song, Nianbo Liu. Distinguishing uncertain objects with multiple features for crowdsensing. IEEE Global Communications Conference, Austin, TX, USA, 2014, pp. 2751-2756.
Journal Papers
1. Bin Liu, Li Haolong, Linshuang Kang. Tangency Portfolios Using Graph Neural Networks. Neural Networks, vol. 193, 108043, 2025, https://doi.org/10.1016/j.neunet.2025.108043
2. Zhizhong Tan, Siyang Liu, Qiang Liu, Min Hu, Xiang Zhang, Wenyong Wang, Bin Liu*. Modeling ESG-driven industrial value chain dynamics using directed graph neural networks. Financial Innovation, vol. 11, 113, 2025, http://dx.doi.org/10.1186/s40854-025-00783-y
3. Min Hu, Zhizhong Tan, Bin Liu*, and Guosheng Yin. Graph Portfolio: High-Frequency Factor Predictors via Heterogeneous Continual GNNs. IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 7, pp. 4104-4116, July 2025, doi: 10.1109/TKDE.2025.3566111.
4. Min Hu, Bin Liu*, Guosheng Yin. Multi-Site and Multi-Pollutant Air Quality Data Modeling. Sustainability, vol. 16(1), 165. 2024.
5. Bin Liu, Guosheng Yin. Graphmax for Text Generation. Journal of Artificial Intelligence Research, vol. 78, pp.823-848, Nov. 2023.
6. Zhuo Tan, Yifan Zhu, Bin Liu*. Learning spatial-temporal feature with graph product. Signal Processing, 210 (2023): 109062.
7. Shaogao Lv, Linsen Wei, Qian Zhang, Bin Liu, Zenglin Xu. “Improved Inference for Imputation-Based Semisupervised Learning Under Misspecified Setting”, IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 11, pp. 6346-6359, Nov. 2022.
8. Liyuan Zheng, Yajie Hu, Bin Liu*, and Wei Deng. “Learning robust word representation over a semantic manifold.” Knowledge-Based Systems, 192 (2020): 105358.
9. Zenglin Xu, Bin Liu*, Shandian Zhe, Haoli Bai, Zihan Wang, Jennifer Neville. Variational random function model for network modeling, IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 1, pp. 318-324, Jan. 2019.
10. Bin Liu, Lirong He, Yingming Li, Shandian Zhe, Zenglin Xu. NeuralCP: Bayesian Multiway Data Analysis with Neural Tensor Decomposition, Cognitive Computation, 10, pp.1051–1061, 2018.
11. He Lirong, Bin Liu, Guangxi Li, Yongpan Sheng, Yafang Wang, and Zenglin Xu. [Knowledge base completion by variational bayesian neural tensor decomposition], Cognitive Computation, 10 (2018): 1075-1084.
12. Bin Liu, Yingming Li, Zenglin Xu. Manifold regularized matrix completion for multi-label learning with ADMM. Neural Networks, vol. 101, pp. 57-67, 2018.
13. Bin Liu, Zenglin Xu, Shuang Wu, Fei Wang. Manifold regularized matrix completion for multilabel classification. Pattern Recognition Letters, Vol. 80, pp. 58-63, 2016.
1. Chunhong Ye, Bin Liu*. Exploring Asset Pricing Through Industrial Chain Dynamics: Insights from Graph Machine Learning. SSRN Electronic Journal (2025)
2. Lu Wei, Yi Huang, Guosheng Yin, Fode Zhang, Manxue Zhang, Bin Liu*. Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph. arXiv preprint arXiv:2410.07138 (2024).
3. Ling Xiang, Quan Hu, Xiang Zhang, Wei Lan, Bin Liu*. Graph Neural Poisson Models for Supply Chain Relationship Forecasting. arXiv preprint arXiv:2508.12044 (2025).
4. Yuedi Zhang, Zhixiang Xia, Guosheng Yin, Bin Liu*. Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images arXiv preprint arXiv:2509.11034 (2025)
Conference Papers
1. Bin Liu, Yu Liu, Zhiqian Li, Jianghe Xiao, Huazhen Lin, Guosheng Yin. Automatic radiotherapy treatment planning with deep functional reinforcement learning. KDD, 2025, Toronto, ON, Canada, pp. 2426-2435.
2. Zhizhong Tan, Min Hu, Bin Liu*, Guosheng Yin. Futures Quantitative Investment with Heterogeneous Continual Graph Neural Networks. ICDM, 2024, Abu Dhabi, UAE, pp. 851-856
3. Jinjin Li, Bin Liu*, Chengyan Liu, Hongli Zhang. Predicting Housing Transaction with Common Covariance GNNs. IJCAI, 2024, Jeju, Korea, pp. 7323-7330.
4. Jiujun He, Bin Liu, Guosheng Yin. Enhancing Semi-supervised Domain Adaptation via Effective Target Labeling. AAAI, 2024, Vancouver, Canada, pp. 12385-12393.
5. Bin Liu, Jiujun He, Ziyuan Li, Xiaoyang Huang, Xiang Zhang, Guosheng Yin. Interpret ESG Rating’s Impact on the Industrial Chain Using Graph Neural Networks. IJCAI, 2023, Macao, China, pp. 6076-6084.
6. Lu Wei, Bin Liu*, Jiujun He, Manxue Zhang, Yi Huang. Autistic Spectrum Disorders Diagnose with Graph Neural Networks. ACM Multimedia, 2023, Ottawa, Canada, pp. 8819–8827.
7. Zhuo Tan, Bin Liu*, Guosheng Yin. Asymmetric Self-Supervised Graph Neural Networks. IEEE International Conference on Big Data, Osaka, Japan, 2022, pp. 1369-1376.
8. Jiujun He, Bin Liu, Xuan Yang. Non-local Patch Mixup for Unsupervised Domain Adaptation. IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, 2022, pp. 969-974.
9. Bin Liu, Wang Liang, Yin Guosheng. Learning distributed sentence vectors with bi-directional 3D convolutions. The 28th International Conference on Computational Linguistics(COLING), Barcelona, Spain, 2020, pp. 6820–6830.
10. Bin Liu, Xiaoxue Gao, Mengshuang He, Lin Liu, Guosheng Yin. A Fast Online COVID-19 Diagnostic System with Chest CT Scans. The 26TH ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Health Day), 2020, Virtual Conference. The online diagnosis system:https://www.covidct.cn/
11. Bin Liu, Guosheng Yin. Chinese document classification with bi-directional convolutional language model. The 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Xi’an, China, 2020, pp. 1785–1788.
12. Bin Liu, Zenglin Xu, Bo Dai, Haoli Bai, Xianghong Fang, Yazhou Ren, Shandian Zhe. Learning from semantically dependent multi-tasks. International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 2017, pp. 3498-3505.
13. Haoli Bai, Zenglin Xu, Bin Liu, Yingming Li. Hierarchical probabilistic matrix factorization with network topology for multi-relational social network. Proceedings of The 8th Asian Conference on Machine Learning, PMLR 63:270-285, 2016.
14. Bin Liu, Chao Song, Nianbo Liu. Distinguishing uncertain objects with multiple features for crowdsensing. IEEE Global Communications Conference, Austin, TX, USA, 2014, pp. 2751-2756.
Journal Papers
1. Bin Liu, Li Haolong, Linshuang Kang. Tangency Portfolios Using Graph Neural Networks. Neural Networks, vol. 193, 108043, 2025, https://doi.org/10.1016/j.neunet.2025.108043
2. Zhizhong Tan, Siyang Liu, Qiang Liu, Min Hu, Xiang Zhang, Wenyong Wang, Bin Liu*. Modeling ESG-driven industrial value chain dynamics using directed graph neural networks. Financial Innovation, vol. 11, 113, 2025, http://dx.doi.org/10.1186/s40854-025-00783-y
3. Min Hu, Zhizhong Tan, Bin Liu*, and Guosheng Yin. Graph Portfolio: High-Frequency Factor Predictors via Heterogeneous Continual GNNs. IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 7, pp. 4104-4116, July 2025, doi: 10.1109/TKDE.2025.3566111.
4. Min Hu, Bin Liu*, Guosheng Yin. Multi-Site and Multi-Pollutant Air Quality Data Modeling. Sustainability, vol. 16(1), 165. 2024.
5. Bin Liu, Guosheng Yin. Graphmax for Text Generation. Journal of Artificial Intelligence Research, vol. 78, pp.823-848, Nov. 2023.
6. Zhuo Tan, Yifan Zhu, Bin Liu*. Learning spatial-temporal feature with graph product. Signal Processing, 210 (2023): 109062.
7. Shaogao Lv, Linsen Wei, Qian Zhang, Bin Liu, Zenglin Xu. “Improved Inference for Imputation-Based Semisupervised Learning Under Misspecified Setting”, IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 11, pp. 6346-6359, Nov. 2022.
8. Liyuan Zheng, Yajie Hu, Bin Liu*, and Wei Deng. “Learning robust word representation over a semantic manifold.” Knowledge-Based Systems, 192 (2020): 105358.
9. Zenglin Xu, Bin Liu*, Shandian Zhe, Haoli Bai, Zihan Wang, Jennifer Neville. Variational random function model for network modeling, IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 1, pp. 318-324, Jan. 2019.
10. Bin Liu, Lirong He, Yingming Li, Shandian Zhe, Zenglin Xu. NeuralCP: Bayesian Multiway Data Analysis with Neural Tensor Decomposition, Cognitive Computation, 10, pp.1051–1061, 2018.
11. He Lirong, Bin Liu, Guangxi Li, Yongpan Sheng, Yafang Wang, and Zenglin Xu. [Knowledge base completion by variational bayesian neural tensor decomposition], Cognitive Computation, 10 (2018): 1075-1084.
12. Bin Liu, Yingming Li, Zenglin Xu. Manifold regularized matrix completion for multi-label learning with ADMM. Neural Networks, vol. 101, pp. 57-67, 2018.
13. Bin Liu, Zenglin Xu, Shuang Wu, Fei Wang. Manifold regularized matrix completion for multilabel classification. Pattern Recognition Letters, Vol. 80, pp. 58-63, 2016.
18’Fall: 统计学习;机器学习;
19’Fall:Python 编程;最优化理论 I;数据科学基础;
20’Fall:最优化理论 I/II;深度学习;数据科学实战;数据科学基础;
21’Spring:自然语言处理;
21’Fall:最优化理论 I/II;
22’Spring:最优化理论 II;
22’Fall:最优化理论 I;
23’Fall:Multivariate Statistical Analysis(多元统计分析);
24’Spring:最优化理论 II;General Linear Model(广义线性模型);
24’Fall:The Introduction of Statitics
19’Fall:Python 编程;最优化理论 I;数据科学基础;
20’Fall:最优化理论 I/II;深度学习;数据科学实战;数据科学基础;
21’Spring:自然语言处理;
21’Fall:最优化理论 I/II;
22’Spring:最优化理论 II;
22’Fall:最优化理论 I;
23’Fall:Multivariate Statistical Analysis(多元统计分析);
24’Spring:最优化理论 II;General Linear Model(广义线性模型);
24’Fall:The Introduction of Statitics
2024.07, Interpret How External Shocks Affect Industrial Chain using Graph Machine Learning, The 7th International Conference on Econometrics and Statistics (EcoSta 2024), Beijing.
2023.08, Customizing personal large-scale language model using co-occurrence statistic information, 首届机器学习与统计会议，华东师范大学，上海.
2023.08, Customizing personal large-scale language model using co-occurrence statistic information, 首届机器学习与统计会议，华东师范大学，上海.