领域知识群落的演变模式与知识传承
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摘要
文章采用社群发现算法对领域知识网络巨分支内部的群落结构进行识别与提取,通过知识群落组成率、输出率以及群落间传承强度的计算与分析,对知识群落的演变模式与知识传承特征进行时间序列的动态跟踪与分析。研究结果表明,领域知识网络在发展演变过程中更多地处于非连通状态;领域知识发展越成熟群落之间的知识交叉融合越频繁;交叉融合性的知识传承多发生在巨分支内部的知识群落之间。
The present research uses community discovery algorithm to identify and extract the community structureinside the giant branches of domain knowledge networks. Through the calculation and analysis on inscape of knowledgecommunities, export of knowledge communities and lineage between communities, the time series dynamic trackingand analysis on evolution pattern and knowledge lineage characteristics of knowledge communities are carried out. Theresearch results show that the domain knowledge networks are more non-connected in the process of evolution; themore mature the domain knowledge develops, the more frequently the knowledge cross-fusion between communities;the cross-fusion knowledge lineage mostly occurs between the knowledge communities within the giant branch.
The present research uses community discovery algorithm to identify and extract the community structureinside the giant branches of domain knowledge networks. Through the calculation and analysis on inscape of knowledgecommunities, export of knowledge communities and lineage between communities, the time series dynamic trackingand analysis on evolution pattern and knowledge lineage characteristics of knowledge communities are carried out. Theresearch results show that the domain knowledge networks are more non-connected in the process of evolution; themore mature the domain knowledge develops, the more frequently the knowledge cross-fusion between communities;the cross-fusion knowledge lineage mostly occurs between the knowledge communities within the giant branch.
引文
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[2]Lewis T G.网络科学:原理与应用[M].陈向阳,巨修练,等译.北京:机械工业出版社,2011:4-5.
[3]Ravasz E,Somera A L,Mongru D A,et al.Hierarchical organization of modularity in metabolic networks[J].Science,2002,297(5586):1551-1555.
[4]Gui X,Li L,Cao J,et al.Dynamic communities in stock market[J].Abstract and Applied Analysis,2014,2014(10):1-9.
[5]Ali A,Qadir J,Rasool R U,et al.Big data for development:applications and techniques[J].Big Data Analytics,2016,1(1):2.
[6]Newman M E J,Girvan M.Finding and evaluating community structure in networks[J].Physical Review E,2004,69(2):026113.
[7]Fudholi D H,Rahayu W,Pardede E.A data-driven dynamic ontology[J].Journal of Information Science,2015,41(3):383-398.
[8]Wallace M L,Gingras Y,Duhon R.A new approach for detecting scientific specialties from raw cocitation networks[J].Journal of the American Society for Information Science and Technology,2009,60(2):240-246.
[9]Yi S,Choi J.The organization of scientific knowledge:the structural characteristics of keyword networks[J].Scientometrics,2012,90(3):1015-1026.
[10]Dong G,Fan J,Shekhtman L M,et al.Resilience of networks with community structure behaves as if under an external field[J].Proceedings of the National Academy of Sciences,2018,115(27):6911-6915.
[11]Mccain K W.Assessing an author's influence using time series historiographic mapping:the oeuvre of conrad hal waddington(1905-1975)[J].Journal of the American Society for Information Science and Technology,2008,59(4):510-525.
[12]Lancichinetti A,Fortunato S.Consensus clustering in complex networks[J].Scientific Reports,2012,2(13):336.
[13]Liu Q,Liu C,Wang J,et al.Evolutionary link community structure discovery in dynamic weighted networks[J].Physica A:Statistical Mechanics and Its Applications,2017,466(C):370-388.
[14]Wang X,Cheng Q,Lu W.Analyzing evolution of research topics with NEViewer:a new method based on dynamic co-word networks[J].Scientometrics,2014,101(2):1253-1271.
[15]刘自强,王效岳,白如江.语义分类的学科主题演化分析方法研究:以我国图书情报领域大数据研究为例[J].图书情报工作,2016,60(15):76-85,93.
[16]朱梦娴,程齐凯,陆伟.基于社会网络的学科主题聚类研究[J].情报杂志,2012,31(11):40-44.
[17]李纲,李岚凤,毛进,等.作者合著网络中研究兴趣相似性实证研究[J].图书情报工作,2015,59(2):75-81.
[18]白如江,冷伏海.k-clique社区知识创新演化方法研究[J].图书情报工作,2013,57(17):86-94.
[19]程齐凯,王晓光.一种基于共词网络社区的科研主题演化分析框架[J].图书情报工作,2013(8):91-96.
[20]滕广青.Folksonomy模式中紧密型领域知识群落动态演化研究[J].中国图书馆学报,2016,42(4):51-63.
[21]Newman M E J.网络科学引论[M].郭世泽,陈哲,译.北京:电子工业出版社,2014:80-90,152-160.
[22]Blondel V D,Guillaume J L,Lambiotte R,et al.Fast unfolding of communities in large networks[J].Journal of Statistical Mechanics:Theory and Experiment,2008(10):P10008.
[23]汪小帆,李翔,陈关荣.网络科学导论[M].北京:高等教育出版社,2012:130-140.
[24]Tang L,Liu H.社会计算:社区发现和社会媒体挖掘[M].文益民,闭应洲,译.北京:机械工业出版社,2013:20-30.