Awareness and Analysis of the Structure of the Information Synergy Network in China s Smart Government
Hu Mo1, Ma Jie1,2, Zhang Yunkai1, Wu Bo3
1.School of Management, Jilin University, Changchun 130022 2.Information Resources Research Center, Jilin University, Changchun 130022 3.School of Human Sciences, Waseda University, Tokyo 163-8001
胡漠, 马捷, 张云开, 武博. 我国智慧政府信息协同网络结构识别与分析[J]. 情报学报, 2020, 39(1): 47-56.
Hu Mo, Ma Jie, Zhang Yunkai, Wu Bo. Awareness and Analysis of the Structure of the Information Synergy Network in China s Smart Government. 情报学报, 2020, 39(1): 47-56.
1 Kim S T. Next generation e-government strategies and asks for the smart society – Based on Korea’s case[J]. Journal of E-Governance, 2013, 36(1): 12-24. 2 AlEnezi A,AlMeraj Z,Manuel P. Challenges of IoT based smart-government development[C]// Proceedings of the 21st Saudi Computer Society National Computer Conference. New York: IEEE, 2018: 155-160. 3 陈锐, 贾晓丰, 赵宇. 智慧城市运行管理的信息协同标准体系[J]. 城市发展研究, 2015, 22(6): 40-46. 4 决胜全面建成小康社会 夺取新时代中国特色社会主义伟大胜利——习近平在中国共产党第十九次全国代表大会上的报告[N]. 人民日报，2017-10-28(1). 5 建设人民满意的服务型政府[N]. 人民日报, 2018-9-9(5). 6 宋懿, 安小米, 范灵俊, 等. 大数据时代政府信息资源共享的协同机制研究——基于宁波市海曙区政府信息资源中心的案例分析[J]. 情报理论与实践, 2018, 41(6): 64-69. 7 张建光, 朱建明, 尚进. 国内外智慧政府研究现状与发展趋势综述[J]. 电子政务, 2015(8): 72-79. 8 陈锐, 贾晓丰, 赵宇. 大数据时代的城市运行管理信息协同模式研究[J]. 中国科学院院刊, 2014, 29(6): 708-717. 9 安小米, 郭明军, 魏玮. 政务信息系统整合共享工程中的协同创新共同体能力构建研究[J]. 情报理论与实践, 2019, 42(4): 76-82. 10 马捷, 蒲泓宇, 张云开, 等. 基于关联数据的政府智慧服务框架与信息协同机制[J]. 情报理论与实践, 2018, 41(11): 20-26. 11 胡漠, 马捷. 信息协同视角下无边界化智慧政务推进机制研究[J]. 情报资料工作, 2019(1): 44-51. 12 朱涛, 常国岑, 张水平, 等. 基于复杂网络的指挥控制信息协同模型研究[J]. 系统仿真学报, 2008, 20(22): 6058-6060, 6065. 13 范如国. 复杂网络结构范型下的社会治理协同创新[J]. 中国社会科学, 2014(4): 98-120, 206. 14 高杉尚孝. 麦肯锡问题分析与解决技巧[M]. 郑舜珑, 译. 北京: 北京时代华文书局, 2014: 19-20. 15 Niboonkit S,Krathu W,Padungweang P. Automatic discovering success factor relationship entities in articles using named entity recognition[C]// Proceedings of the International Conference on Knowledge and Smart Technology. New York: IEEE, 2017: 238-241. 16 Hkiri E,Mallat S,Zrigui M. Integrating bilingual named entities lexicon with conditional random fields model for Arabic named entities recognition[C]// Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition. New York: IEEE, 2017: 609-614. 17 Wang S W,Xu R F,Liu B. Financial named entity recognition based on conditional random fields and information entropy[C]// Proceedings of the International Conference on Machine Learning and Cybernetics. New York: IEEE, 2014: 838-843. 18 Gong L J,Sun X. ATRMiner: A system for automatic biomedical named entities recognition[C]// Proceedings of the International Conference on Natural Computation. New York: IEEE, 2010: 3842-3845. 19 刘晓娟, 刘群, 余梦霞. 基于关联数据的命名实体识别[J]. 情报学报, 2019, 38(2): 191-200. 20 Fu G H. Chinese named entity recognition using a morpheme-based chunking tagger[C]// Proceedings of the International Conference on Asian Language Processing. New York: IEEE, 2009: 289-292. 21 Hoff P D,Raftery A E,Handcock M S. Latent space approaches to social network analysis[J]. Journal of the American Statistical Association, 2002, 97(460): 1090-1098. 22 Peng S D. Cohesive subgroups analysis of asynchronous cognitive interactive network in collaborative learning[C]// Proceedings of the International Conference on Electrical and Control Engineering. New York: IEEE, 2011: 6424-6428. 23 Aksentijevi? S,Markovi? D,Tijan E, et al. Application of social network analysis to port community systems[C]// Proceedings of the 41st International Convention on Information and Communication Technology, Electronics and Microelectronics. New York: IEEE, 2018: 1304-1310. 24 Wang C S,Ting I H,Li Y C. Taiwan academic network discussion via social networks analysis perspective[C]// Proceedings of the International Conference on Advances in Social Networks Analysis and Mining. New York: IEEE, 2011: 685-689. 25 Khonsari K K,Nayeri Z A,Fathalian A, et al. Social network analysis of Iran s green movement opposition groups using Twitter[C]// Proceedings of the International Conference on Advances in Social Networks Analysis and Mining. New York: IEEE, 2010: 414-415. 26 张雪, 张志强, 陈秀娟. 基于期刊论文的作者合作特征及其对科研产出的影响——以国际医学信息学领域高产作者为例[J]. 情报学报, 2019, 38(1): 29-37. 27 Akhtar N. Social network analysis tools[C]// Proceedings of the Fourth International Conference on Communication Systems and Network Technologies. New York: IEEE, 2014: 388-392. 28 Li H. Centrality analysis of online social network big data[C]// Proceedings of the 3rd International Conference on Big Data Analysis. New York: IEEE, 2018: 38-42. 29 (两会受权发布)关于国务院机构改革方案的说明[EB/OL]. (2018-03-14) [2019-03-06]. http://www.xinhuanet.com/politics/2018lh/2018-03/14/c_1122533011.htm. 30 Yao X Y. A method of Chinese organization named entities recognition based on statistical word frequency, part of speech and length[C]// Proceedings of the 4th IEEE International Conference on Broadband Network and Multimedia Technology. New York: IEEE, 2011: 637-641. 31 赫南, 李德毅, 淦文燕, 等. 复杂网络中重要性节点发掘综述[J]. 计算机科学, 2007, 34(12): 1-5, 17. 32 Comin C H,da Fontoura Costa L. Identifying the starting point of a spreading process in complex networks[J]. Physical Review E, 2011, 84(5): 056105. 33 Kitsak M,Gallos L K,Havlin S, et al. Identification of influential spreaders in complex networks[J]. Nature Physics, 2010, 6(11): 888-893. 34 Borge-Holthoefer J,Moreno Y. Absence of influential spreaders in rumor dynamics[J]. Physical Review E, 2012, 85(2): 026116. 35 Wasserman S,Faust K. Social network analysis: Methods and applications[M]. Cambridge: Cambridge University Press, 1994: 199. 36 Freeman L C. Centrality in social networks’ conceptual clarification[J]. Social Networks, 1978, 1(3): 215-239. 37 Balasundaram B. Cohesive subgroup model for graph-based text mining[C]// Proceedings of the International Conference on Automation Science and Engineering. New York: IEEE, 2008: 989-994. 38 李亮, 朱庆华. 社会网络分析方法在合著分析中的实证研究[J]. 情报科学. 2008, 26(4): 549-555. 39 Wang Z,Chen Q,Hou B, et al. Parallelizing maximal clique and k-plex enumeration over graph data[J]. Journal of Parallel and Distributed Computing, 2017, 106: 79-91. 40 Wu Y,Xue Y Z,Xue Z L. The study on the core personality trait words of Chinese medical university students based on social network analysis[J]. Medicine, 2017, 96(37): e8078.
