陈虹枢
陈虹枢，硕士生导师，北京理工大学管理与经济学院管理科学与物流系助理教授、特别副研究员，主要研究方向为科技文本挖掘、知识发现、技术预测、创新管理与科技评价。2011年起参与北京理工大学-悉尼科技大学博士生双学位项目，2015年、2016年分别于北京理工大学、悉尼科技大学获得管理学博士学位及计算机科学信息系统方向博士学位，曾任悉尼科技大学工程与信息学院科研助理及助教。
目前已在TCYB、TFSC、TEM、KBS等国际期刊、会议和著作中发表论文20余篇（含ESI高被引1篇），并担任多个SCI/SSCI国际期刊的论文评阅人，为Technological Forecasting and Social Change 及 Knowledge-Based Systems 国际期刊的杰出评阅人，主持和参与国家自然科学基金项目共2项，曾担任国际会议IEEE SMC-2013和ISKE-2019的分会主席。
研究兴趣
基于主题模型的文献数据挖掘
基于复杂网络的科学文献数据分析
面向深度学习的文本挖掘与知识发现
代表论文
[1]Chen, H., Wang, X., Pan, S., Xiong, F.* (2019). Identify Topic Relations in Scientific Literature Using Topic Modeling. IEEE Transactions on Engineering Management, In Press. (SCI期刊，影响因子1.867)
[2]Chen, H.*, Zhang, G., Zhu, D., Lu, J. (2017). Topic-based technological forecasting based on patent data: A case study of Australian patents from 2000 to 2014. Technological Forecasting and Social Change, 119, 39-52. (SSCI期刊，影响因子3.815)
[3]Chen, H., Zhang, G., Zhu, D., Lu, J.* (2015). A patent time series processing component for technology intelligence by trend identification functionality. Neural Computing and Applications, 26(2), 345-353. (SCI期刊，影响因子4.664)
[4]Xiong, F., Shen, W., Chen, H.*, Pan, S., Wang, X., Yan, Z. (2019) Exploiting Implicit Influence from Information Propagation for Social Recommendation. IEEE Transactions on Cybernetics, In Press. (SCI期刊，影响因子10.387)
[5]Li, Z., Xiong, F., Wang, X., Chen, H., & Xiong, X. (2019).Topological Influence-Aware Recommendation on Social Networks. Complexity, 2019, Article ID **, 12 pages. (SCI期刊，影响因子2.591)
[6]Zhang, Y.，Lu, J., Liu, F., Liu, Q., Porter, A., Chen, H.*, Zhang, G. (2018). Does deep learning help topic extraction? A kernel k-means clustering method with word embedding, Journal of Informetrics, 12(4), 1099-1117. (SCI期刊，影响因子3.879)
[7]Cai, Y., Pan, S.*, Wang, X., Chen, H.*, Cai, X., Zuo, M. (2018).Measuring Distance-based Semantic Similarity Using Meronymy and Hyponymy Relations, Neural Computing and Applications, In Press. (SCI期刊，影响因子4.664)
[8]Zhang, Y., Chen, H.*, Lu, J., Zhang, G. (2017). Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016. Knowledge-Based Systems, 133, 255-268. (SCI期刊，影响因子5.101)
[9]Wang, X., Liu, Y.*, Zhang, G., Zhang, Y., Chen, H., & Lu, J. (2017). Mixed Similarity Diffusion for Recommendation on Bipartite Networks. IEEE Access, 5, 21029-21038. (SCI期刊，影响因子4.098)
[10]Zhang, Y. *, Zhang, G., Chen, H., Porter, A., Zhu, D., Lu, J.(2016). Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research. Technological Forecasting and Social Change, 105, 179-191. (ESI高被引论文，SSCI期刊，影响因子3.815)
[11]Chen, H., Song, Y., Wang, X., Wang, X., Wang, X., Yu, M. (2019). Research Topic Recommendation based on Latent Dirichlet Allocation. In the 14th International Conference on Intelligent Systems and Knowledge Engineering, In Press (EI会议)
[12]Chen, H., Zhang, Y., Zhang, G., Zhu, D., Lu, J. (2015). Modeling Technological Topic Changes in Patent Claims. In the Proceeding of 2015 Portland International Conference on Management of Engineering and Technology, pp. 2049-2059. (EI、ISTP会议)
[13]Chen, H., Zhang, G., Lu, J., Zhu, D. (2015). A Fuzzy Approach for Measuring Development of Topics in Patents Using Latent Dirichlet Allocation. In the 2015 IEEE International Conference on Fuzzy Systems, p. **. (EI、ISTP会议)
[14]Chen, H., Zhang, G., Lu, J. (2013). A Time-Series-Based Technology Intelligence Framework by Trend Prediction Functionality. In the 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3477-3482. (EI、ISTP会议)
[15]Chen, H., Zhang, G., Lu, J., Zhu, D. (2014). A Two-steps Agglomerative Hierarchical Clustering Method for Patent Time-dependent Data. In the 7th International Conference on Intelligent Systems and Knowledge Engineering, pp. 111-121. (EI会议)
[16]Chen, H., et al. (2011). Evaluating Olympic Technology Development: Use of Literature Analysis Based on Science Citation Index. In the International Conference on Computer Communication and Management, pp. 436-440. (ISTP会议)
[17] Chen, H., et al. (2016). Identifying Technological Topic Changes in Patent Claims Using Topic Modeling. In Anticipating Future Innovation Pathways through Large Data Analytics, pp. 187-209, Springer.
科研项目
国家自然科学基金, 国家科学基金资助成效及科技人才计划总体情况跟踪分析（2019-2020）, 在研，主持
国家自然科学基金, “卡脖子”关键核心技术领域的创新能力与形势研判分析（2019-2020），在研，参与
联系方式
北京市海淀区中关村南大街5号北京理工大学管理与经济学院，100081
电话：
邮箱: hongshu.chen@bit.edu.cn