Please wait a minute...
New Technology of Library and Information Service  2009, Vol. Issue (10): 56-61    DOI: 10.11925/infotech.1003-3513.2009.10.10
Current Issue | Archive | Adv Search |
Domestic Information Services Research Concept Network Analysis Based on Complex Network Method
Wang Jiandong
(Department of Information Management, Peking University, Beijing 100871,China)
Export: BibTeX | EndNote (RIS)      

 Based on the keywords in the 11 261 papers in the field of information services from CNKI, this paper constructs an undirected weighting network which contains 6 401 vertices(keywords) and 21 007 edges using co-word analysis, and verifies that the network has the characters of scale free and small world. The index of degree centrality and betweenness centrality of vertices in the network are calculated, and a method of detecting cross concept in the network is introduced. Finally, using the G-N clustering algorithm, the paper performs a cluster analysis on the domestic information services research concept network, and divides the research field into 7 different branches.

Key wordsInformation services      Complex network      Co-word analysis      Degree centrality      Betweenness centrality      G-N clustering algorithm     
Received: 24 June 2009      Published: 25 October 2009


Corresponding Authors: Wang Jiandong     E-mail:
About author:: Wang Jiandong

Cite this article:

Wang Jiandong. Domestic Information Services Research Concept Network Analysis Based on Complex Network Method. New Technology of Library and Information Service, 2009, (10): 56-61.

URL:     OR

[1] 陈建龙.信息服务模式研究[J].北京大学学报:哲学社会科学版,2003,40(3):124-132.
[2] Networks/Pajek[EB/OL].[2009-08-29].
[3] 王建冬.博客空间中的角色扮演现象研究[D].北京: 北京大学, 2008.
[4] 王建冬,王继民,田飞佳.博客圈的特征及其演化机制初探[J].现代图书情报技术,2008(4):56-60.
[5] 王建冬,孙慧明.基于网站链接分析的“211”高校排名实证研究[J].现代图书情报技术,2008(9):64-69.
[6] 王继民,王建冬,田飞佳.三国人物网络的拓扑结构特征[C].见:第三届社会网与关系管理研讨会议论文集, 南京. 2007.
[7]  罗家德.社会网分析讲义[M].北京: 社会科学文献出版社, 2005.
[8]  吴金闪,狄增如. 从统计物理学看复杂网络研究[J].物理学进展, 2003,24(1):18-46.
[9] 汪小帆,李翔, 陈关荣.复杂网络理论及其应用[M].北京:清华大学出版社,2006.
[10] Ramon Ferrer i Cancho,Ricard V Solé.The Small World of Human Language[J]. Proceedings of the Royal Society of London,Series B: Biological Sciences,2001,265(1452):2261-2265.
[11] 赵鹏,蔡庆生,王清毅,等.一种基于复杂网络特征的中文文档关键词抽取算法[J].模式识别与人工智能,2007,20(6):827-831.
[12] 王建冬.信息的社会性分析:起源、理论与应用[J].图书情报知识,2009(4):36-43.
[13] 刘军.社会网络分析导论[M]. 北京: 社会科学文献出版社,2004.
[14]  De Nooy W, Mrvar A,Batagelj V. Exploratory Social Network Analysi with Pajek[M]. Cambridge University Press, 2005.
[15] 殷国鹏,莫云生,陈禹. 利用社会网络分析促进隐性知识管理[J]. 清华大学学报:自然科学版,2006,46(1):964-969.
[16] Girvan M, Newman M E J. Community Structure in Social and Biological Network[C]. In:Proceedings of the National Academy of Sciences of the United States of America.2002(99):7821-7826.
[17] 莫春玲. 复杂网络中聚类方法及社团结构的研究[D]. 武汉:武汉理工大学, 2007.

[1] Chen Wenjie,Wen Yi,Yang Ning. Fuzzy Overlapping Community Detection Algorithm Based on Node Vector Representation[J]. 数据分析与知识发现, 2021, 5(5): 41-50.
[2] Wu Jinming,Hou Yuefang,Cui Lei. Automatic Expression of Co-occurrence Clustering Based on Indexing Rules of Medical Subject Headings[J]. 数据分析与知识发现, 2020, 4(9): 133-144.
[3] Su Qing,Chen Sizhao,Wu Weimin,Li Xiaomei,Huang Tiankuan. Personalized Recommendation Model Based on Collaborative Filtering Algorithm of Learning Situation[J]. 数据分析与知识发现, 2020, 4(5): 105-117.
[4] Li Wenzheng,Gu Yijun,Yan Hongli. Predicting Community Numbers with Network Bayesian Information Criterion[J]. 数据分析与知识发现, 2020, 4(4): 72-82.
[5] Qikai Cheng,Jiamin Wang,Wei Lu. Discovering Domain Vocabularies Based on Citation Co-word Network[J]. 数据分析与知识发现, 2019, 3(6): 57-65.
[6] Xiang Li,Xiaodong Qian. Research on Impact of Commodity Online Evaluation for Consumption Convergence[J]. 数据分析与知识发现, 2019, 3(3): 102-111.
[7] Jiao Yan,Jing Ma,Kang Fang. Computing Text Semantic Similarity with Syntactic Network of Co-occurrence Distance[J]. 数据分析与知识发现, 2019, 3(12): 93-100.
[8] Wuxuan Jiang,Huixiang Xiong,Jiaxin Ye,Ning An. Creating Dynamic Tags for Social Networking Groups[J]. 数据分析与知识发现, 2019, 3(10): 98-109.
[9] Qian Xiaodong,Li Min. Identifying E-commerce User Types Based on Complex Network Overlapping Community[J]. 数据分析与知识发现, 2018, 2(6): 79-91.
[10] Chen Yunwei,Zhang Ruihong. Comparing on Community Detection Algorithms for Information Mining[J]. 数据分析与知识发现, 2018, 2(10): 84-94.
[11] Liu Bingyao,Ma Jing,Li Xiaofeng. Topic Representation Model Based on “Feature Dimensionality Reduction”[J]. 数据分析与知识发现, 2017, 1(11): 53-61.
[12] Wu Jiang,Chen Jun,Zhang Jinfan. A Knowledge Supply-Demand Simulation System for Collaborative Innovation[J]. 现代图书情报技术, 2016, 32(9): 27-33.
[13] Ye Teng,Han Lichuan,Xing Chunxiao,Zhang Yan. Knowledge Dissemination Mechanism in Virtual Communities: Case Study Based on Complex Network Theory[J]. 现代图书情报技术, 2016, 32(7-8): 70-77.
[14] Hong Ma, Yongming Cai. A CA-LDA Model for Chinese Topic Analysis: Case Study of Transportation Law Literature[J]. 数据分析与知识发现, 2016, 32(12): 17-26.
[15] Lixin Xia,Ying Tan. Analysis and Visualization of the LOD Network Structure[J]. 现代图书情报技术, 2016, 32(1): 65-72.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938