基于引文网络重叠社团发现的图书情报领域学科主题结构分析

doi:10.3772/j.issn.1000-0135.2020.10.002

情报学报

2020, Vol. 39

Issue (10): 1021-1033 DOI: 10.3772/j.issn.1000-0135.2020.10.002

情报理论与应用

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基于引文网络重叠社团发现的图书情报领域学科主题结构分析

王伟^1,2, 杨建林^1,2

1.南京大学信息管理学院，南京 210023
2.江苏省数据工程与知识服务重点实验室，南京 210023

Mapping the Subject Structure of Library and Information Science through Overlapping Community Detection in Citation Network

Wang Wei^1,2, Yang Jianlin^1,2

1.School of Information Management, Nanjing University, Nanjing 210023
2.Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023

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摘要描绘学科主题结构能促进学科理论和方法研究，助力学科创新发展。本文提出基于重叠社团发现算法可视化学科主题结构，利用有向h度测度学科主题间知识流动，以拓扑结构和内容揭示重叠节点角色，并以图书馆学和情报学为研究对象检验了方法的有效性。结果表明，这一方法能够挖掘出细粒度的研究主题，展现学科主题结构。情报学和图书馆学理论研究、知识管理、引文分析、智慧图书馆、图书馆学科服务等主题与其他研究主题连接紧密。重叠节点分化为具有高介数中心性和度的核心节点，以及低介数中心性和度的边缘节点。情报学和图书馆学学科界限模糊，研究主题呈现同质和分野的特点，在深化学科理论研究的基础上促进学科深度融合将有助于学科发展。

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	王伟
	杨建林

关键词 ：引文网络, 学科主题结构, 重叠社团发现, 有向h度, 重叠节点

收稿日期: 2019-08-09

基金资助:国家社会科学基金重大项目“情报学学科建设与情报工作未来发展路径研究”（17ZDA291）。

作者简介: 王伟，男，1994年生，博士研究生，研究方向为信息分析与科学计量、自然语言处理，E-mail:dg1914023@smail.nju.edu.cn；杨建林，男，1970年生，博士，教授，博士生导师，研究方向为数据挖掘、信息检索、学术评；

引用本文:

王伟, 杨建林. 基于引文网络重叠社团发现的图书情报领域学科主题结构分析[J]. 情报学报, 2020, 39(10): 1021-1033.
Wang Wei, Yang Jianlin. Mapping the Subject Structure of Library and Information Science through Overlapping Community Detection in Citation Network. 情报学报, 2020, 39(10): 1021-1033.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2020.10.002 或 https://qbxb.istic.ac.cn/CN/Y2020/V39/I10/1021

1 卫军朝, 蔚海燕. 科学结构及演化分析方法研究综述[J]. 图书与情报, 2011(4): 48-52.
2 Figuerola C G, García Marco F J, Pinto M. Mapping the evolution of library and information science (1978-2014) using topic modeling on LISA[J]. Scientometrics, 2017, 112(3): 1507-1535.
3 王芳, 赵洪, 张维冲. 我国情报学科理论研究形态及学术影响力的全数据分析[J]. 图书情报知识, 2018(6): 15-28.
4 情报学与情报工作发展南京共识[J]. 情报学报, 2017, 36(11): 1209-1210.
5 罗双玲, 张文琪, 夏昊翔. 基于半积累引文网络社区发现的学科领域主题演化分析——以“合作演化” 领域为例[J]. 情报学报, 2017, 36(1): 100-110.
6 Palla G, Derényi I, Farkas I,et al. Uncovering the overlapping community structure of complex networks in nature and society[J]. Nature, 2005, 435(7043): 814-818.
7 Hou J H, Yang X C, Chen C M. Emerging trends and new developments in information science: A document co-citation analysis (2009-2016)[J]. Scientometrics, 2018, 115(2): 869-892.
8 Small H. Update on science mapping: Creating large document spaces[J]. Scientometrics, 1997, 38(2): 275-293.
9 Garfield E. Historiographic mapping of knowledge domains literature[J]. Journal of Information Science, 2004, 30(2): 119-145.
10 Small H, Sweeney E, Greenlee E. Clustering the science citation index using co-citations. II. Mapping science[J]. Scientometrics, 1985, 8(5-6): 321-340.
11 Small H, Sweeney E. Clustering the science citation index? using co-citations[J]. Scientometrics, 1985, 7(3-6): 391-409.
12 Kessler M M. Bibliographic coupling between scientific papers[J]. American Documentation, 1963, 14(1): 10-25.
13 Kajikawa Y, Ohno J, Takeda Y,et al. Creating an academic landscape of sustainability science: An analysis of the citation network[J]. Sustainability Science, 2007, 2(2): 221-231.
14 Chen C M. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature[J]. Journal of the American Society for Information Science and Technology, 2006, 57(3): 359-377.
15 van Eck N J, Waltman L. CitNetExplorer: A new software tool for analyzing and visualizing citation networks[J]. Journal of Informetrics, 2014, 8(4): 802-823.
16 马费成, 陈潇俊, 刘向. 基于科学知识图谱分析的知识演化研究——以生物医学为例[J]. 情报科学, 2012, 30(1): 1-7, 15.
17 Shibata N, Kajikawa Y, Takeda Y,et al. Comparative study on methods of detecting research fronts using different types of citation[J]. Journal of the American Society for Information Science and Technology, 2009, 60(3): 571-580.
18 Boyack K W, Klavans R. Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?[J]. Journal of the American Society for Information Science and Technology, 2010, 61(12): 2389-2404.
19 Chakraborty T, Chakraborty A. OverCite: Finding overlapping communities in citation network[C]// Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. New York: ACM Press, 2014: 1124-1131.
20 Gopalan P K, Blei D M. Efficient discovery of overlapping communities in massive networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2013, 110(36): 14534-14539.
21 Lancichinetti A, Fortunato S, Kertész J. Detecting the overlapping and hierarchical community structure in complex networks[J]. New Journal of Physics, 2009, 11(3): 033015.
22 Palla G, Barabási A L, Vicsek T. Quantifying social group evolution[J]. Nature, 2007, 446(7136): 664-667.
23 刘非凡, 李长玲, 魏绪秋. 我国情报学学科主题结构分析[J]. 情报理论与实践, 2015, 38(2): 121-126.
24 Xie J R, Kelley S, Szymanski B K. Overlapping community detection in networks: The state of the art and comparative study[J]. ACM Computing Surveys, 2013, 45(4): 1-35.
25 Harenberg S, Bello G, Gjeltema L,et al. Community detection in large-scale networks: A survey and empirical evaluation[J]. Wiley Interdisciplinary Reviews: Computational Statistics, 2014, 6(6): 426-439.
26 肖雪, 王钊伟, 陈云伟, 等. 基于样本加权的引文网络的社团划分[J]. 图书情报工作, 2016, 60(20): 86-93.
27 Yin J H, Wang J Y. A Dirichlet multinomial mixture model-based approach for short text clustering[C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2014: 233-242.
28 Zuo Y, Zhao J C, Xu K. Word network topic model: A simple but general solution for short and imbalanced texts[J]. Knowledge and Information Systems, 2016, 48(2): 379-398.
29 Qiang J P, Qian Z Y, Li Y, et al. Short text topic modeling techniques, applications, and performance: A survey[OL]. https://arxiv.org/pdf/1904.07695.pdf.
30 Xie J R, Szymanski B K, Liu X M. SLPA: Uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process[C]// Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops. IEEE, 2011: 344-349.
31 Raghavan U N, Albert R, Kumara S. Near linear time algorithm to detect community structures in large-scale networks[J]. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2007, 76(3 Pt 2): 036106.
32 Nicosia V, Mangioni G, Carchiolo V, et al. Extending the definition of modularity to directed graphs with overlapping communities[J]. Journal of Statistical Mechanics: Theory and Experiment, 2009, 2009(3): P03024.
33 Zhao S X, Ye F Y. Exploring the directed h-degree in directed weighted networks[J]. Journal of Informetrics, 2012, 6(4): 619-630.
34 Allen B, Reser D. Content analysis in library and information science research[J]. Library & Information Science Research, 1990, 12(3): 251-262.
35 Lee J W, Yang K D. A bibliometric study of library and information science research in Korea[J]. Journal of the Korean Society for Library and Information Science, 2011, 45(4): 53-76.
36 冷伏海, 于微微. 从2002—2011年ARIST及《情报学进展》载文内容看情报学研究动态与发展[J]. 情报学报, 2013, 32(10): 1012-1025.
37 张雪梅. 我国图书馆学研究的主题分类表构建及主题分布规律研究[D]. 长春: 吉林大学, 2018.
38 赵星, 谭旻, 余小萍, 等. 我国文科领域知识扩散之引文网络探析[J]. 中国图书馆学报, 2012, 38(5): 59-67.
39 李健, 李洋. 我国图书馆学与情报学发展的同归与分野[J]. 情报杂志, 2017, 36(5): 9-13, 89.
40 苏新宁. 大数据时代情报学学科崛起之思考[J]. 情报学报, 2018, 37(5): 451-459.
41 国家图书馆《中国图书馆分类法》编辑委员会. 中国分类主题词表[M]. 第2版. 北京: 北京图书馆出版社, 2005.
42 叶继元. 图书馆学、情报学与信息科学、信息管理学等学科的关系问题[J]. 中国图书馆学报, 2004, 30(3): 11-17, 23.
43 苏新宁. 提升图书情报学学科地位的思考——基于CSSCI的实证分析[J]. 中国图书馆学报, 2010, 36(4): 47-53.