知识组织

基于ERGM的学科交叉领域知识连接机制实证研究

  • 操玉杰 ,
  • 李纲 ,
  • 毛进 ,
  • 杨冠灿
展开
  • 1. 武汉大学信息资源研究中心 武汉 430072;
    2. 中国人民大学信息资源管理学院 北京 100872
操玉杰(ORCID:0000-0002-8899-9626),博士研究生;李纲(ORCID:0000-0001-5573-6400),教授,博士生导师;杨冠灿(ORCID:0000-0002-1706-1884),讲师,博士。

收稿日期: 2018-12-06

  修回日期: 2019-04-21

  网络出版日期: 2019-10-05

基金资助

本文系国家自然科学基金青年项目"基于学术异质网络表示学习的知识群落发现"(项目编号:71804135)和中国博士后科学基金项目"融合语义与关系的科研社群识别与演化研究"(项目编号:2018M630885)研究成果之一。

An Empirical Study on Knowledge Connection Mechanism of Interdisciplinary Field Based on ERGM

  • Cao Yujie ,
  • Li Gang ,
  • Mao Jin ,
  • Yang Guancan
Expand
  • 1. Center for Studies of Information Resources, Wuhan University, Wuhan 430072;
    2. School of Information Resource Management, Renmin University of China, Beijing 100872

Received date: 2018-12-06

  Revised date: 2019-04-21

  Online published: 2019-10-05

Supported by

 

摘要

[目的/意义] 旨在通过探讨学科交叉领域共词网络生成的影响因素及其作用机理,揭示学科交叉领域的微观知识连接机制。[方法/过程] 结合网络嵌入性理论,将学科交叉领域关键词共现关系建立的影响因素归纳为网络结构因素(内生变量)和关键词属性因素(外生变量),进而借助指数随机图模型,选择学科交叉领域"医学信息学"开展实证研究。[结果/结论] 研究结果表明:网络结构对共现关系生成的影响大于关键词本身属性的影响;择优连接机制和传递性机制具有显著正向作用;关键词节点倾向于与较新节点相连;医学信息学的关键词倾向于与基础学科的关键词建立共现关系,而基础学科的关键词却倾向于与自身学科关键词相连。

本文引用格式

操玉杰 , 李纲 , 毛进 , 杨冠灿 . 基于ERGM的学科交叉领域知识连接机制实证研究[J]. 图书情报工作, 2019 , 63(19) : 128 -135 . DOI: 10.13266/j.issn.0252-3116.2019.19.013

Abstract

[Purpose/significance] The article aims to explore the factors and their mechanisms influencing the generation of co-word network for interdisciplinary field, and to reveal micro-level mechanisms of knowledge connection in interdisciplinary field.[Method/process] Borrowing network embedding theory, the article summarizes the factors into network structure factors (endogenous variables) and keywords' attribute factors (exogenous variables). Exponential random graph model is constructed based on these factors to perform an empirical analysis on the field of Medical Informatics.[Result/conclusion] The results show that the influence of network structure factors on the co-occurrence relationship generation is greater than that of keywords' attributes. Preferential attachment and transitive mechanism have significant positive effect. Keywords tend to be connected with the newer ones. In addition, the keywords of Medical Informatics tend to establish co-occurrence relations with the keywords from basic disciplines, while the keywords from basic disciplines tend to be connected with the keywords in their own disciplines. The conclusions are helpful to understand the formation process of knowledge systems in interdisciplinary fields and the interactions of interdisciplinary knowledge.

参考文献

[1] 刘仲林, 赵晓春. 跨学科研究:科学原创性成果的动力之源——以百年诺贝尔生理学和医学奖获奖成果为例[J]. 科学技术与辩证法, 2005, 22(6):105-109.
[2] 许海云, 尹春晓, 郭婷, 等. 学科交叉研究综述[J]. 图书情报工作, 2015,59(5):119-127.
[3] 章成志, 吴小兰. 跨学科研究综述[J]. 情报学报, 2017, 36(5):523-535.
[4] LEYDESDORFF L. Betweenness centrality as an indicator of the interdisciplinarity of scientific journals[J]. Journal of the American Society for Information Science and Technology, 2007, 58(9):1303-1319.
[5] PORTER A, COHEN A, DAVID ROESSNER J, et al. Measuring researcher interdisciplinarity[J]. Scientometrics, 2007, 72(1):117-147.
[6] MORILLO F, BORDONS M, GÍMEZ I. An approach to interdisciplinarity through bibliometric indicators[J]. Scientometrics, 2001, 51(1):203-222.
[7] 马费成, 刘向. 科学知识网络的演化模型[J]. 系统工程理论与实践, 2013, 33(2):437-443.
[8] YAN E, DING Y. Scholarly network similarities:how bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other[J]. Journal of the American Society for Information Science and Technology, 2012, 63(7):1313-1326.
[9] CALLON M, COURTIAL J P, LAVILLE F. Co-word analysis as a tool for describing the network of interactions between basic and technological research:the case of polymer chemsitry[J]. Scientometrics, 1991, 22(1):155-205.
[10] 王晓光. 科学知识网络的形成与演化(Ⅰ):共词网络方法的提出[J]. 情报学报, 2009(4):599-605.
[11] DING Y, CHOWDHURY G G, FOO S. Bibliometric cartography of information retrieval research by using co-word analysis[J]. Information processing & management, 2001, 37(6):817-842.
[12] 胡吉明, 张晓娟, 谭婧. 我国政府信息资源研究的主题结构与演化态势[J]. 信息资源管理学报, 2018, 8(3):54-63, 36.
[13] 李纲, 巴志超. 共词分析过程中的若干问题研究[J]. 中国图书馆学报, 2017, 43(4):93-113.
[14] 许海云, 郭婷, 岳增慧, 等. 基于TI指标系列的情报学学科交叉主题研究[J]. 情报学报, 2015, 34(10):1067-1078.
[15] HU J, ZHANG Y. Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization[J]. Scientometrics, 2017, 112(1):91-109.
[16] 王晓光. 科学知识网络的形成与演化(Ⅱ):共词网络可视化与增长动力学[J]. 情报学报, 2010(2):314-322.
[17] ROBINS G, PATTISON P, KALISH Y, et al. An introduction to exponential random graph (p*) models for social networks[J]. Social networks, 2007, 29(2):173-191.
[18] JIAO C, WANG T, LIU J, et al. Using exponential random graph models to analyze the character of peer relationship networks and their effects on the subjective well-being of adolescents[J]. Frontiers in psychology, 2017, 8:583.
[19] ZHANG C, BU Y, DING Y, et al. Understanding scientific collaboration:Homophily, transitivity, and preferential attachment[J]. Journal of the Association for Information Science and Technology, 2018, 69(1):72-86.
[20] 杨冠灿, 陈亮, 张静, 等. 专利引用关系形成的解释框架:一个指数随机图模型视角[J]. 图书情报工作,2019, 63(5):100-109.
[21] POL J V D. Introduction to network modeling using Exponential Random Graph Models (ERGM):theory and an application using R-Project[J/OL]. Computational Economics, 2018:1-31[2019-03-20]. https://doi.org/10.1007/s10614-018-9853-2.
[22] PENG T Q. Assortative mixing, preferential attachment, and triadic closure:a longitudinal study of tie-generative mechanisms in journal citation networks[J]. Journal of informetrics, 2015, 9(2):250-262.
[23] BARABÁSI A L, RAVASZ E, VICSEK T. Deterministic scale-free networks[J]. Physica A:statistical mechanics and its applications, 2001, 299(3-4):559-564.
[24] 马费成, 刘向. 知识网络的演化(Ⅲ):连接机制[J]. 情报学报, 2011, 30(10):1015-1021.
[25] BIANCONI G, DARST R K, IACOVACCI J, et al. Triadic closure as a basic generating mechanism of communities in complex networks[J]. Physical review E, 2014, 90(4):042806.
[26] 马费成, 刘向. 知识网络的演化(Ⅱ):增长老化与知识产生时点的关系[J]. 情报学报, 2011, 30(9):916-921.
[27] 吕双. 国际知识管理研究的领域分析Ⅱ:学科领域分布的深度挖掘[J]. 情报杂志, 2012, 31(3):118-123.
[28] HANDCOCK M S, HUNTER D R, BUTTS C T, et al. statnet:software tools for the representation, visualization, analysis and simulation of network data[J]. Journal of statistical software, 2008, 24(1):1-9.
[29] ROSE KIM, JI YOUN, HOWARD M, et al. Understanding network formation in strategy research:exponential random graph models[J]. Strategic management journal, 2016, 37(1):22-44.
[30] WASSERMAN S, PATTISON P. Logit models and logistic regressions for social networks:I. An introduction to Markov graphs and p[J]. Psychometrika, 1996, 61(3):401-425.
[31] 齐燕, 许海云, 方曙. 基于WOS数据的医学信息学学科交叉发展态势研究[J]. 中华医学图书情报杂志, 2016, 25(11):30-41.
[32] 邱均平. 信息计量学[M]. 武汉:武汉大学出版社, 2007.
[33] DONOHUE J C. Understanding scientific literature:a bibliographic approach[M]. Massachusetts:The MIT Press, 1973.
[34] BARABÁSI A L. Scale-free networks:a decade and beyond[J]. Science, 2009, 325(5939):412-413.
[35] KWON S. Characteristics of interdisciplinary research in author keywords appearing in Korean journals[J]. Malaysian journal of library & information science, 2018, 23(2):77-93.
文章导航

/