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基于网络结构特征的技术创新合作网络链路可预测性研究 被引量:4

Research on Predictability of Technological Innovation Cooperation Network Links Based on Network Structure Characteristics
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摘要 [目的/意义]网络链路的可预测性问题是复杂网络的研究前沿。文章对技术创新合作网络链路的可预测性进行探究,为技术创新合作对象的预测和推荐提供支撑。[方法/过程]构建基于专利的海洋工程装备技术创新合作网络,采用复杂网络方法对比研究技术创新合作网络与经典复杂网络的结构特征,探究网络链路的可预测性,并在此基础上对网络结构特征与网络链路的可预测性间的关系进行挖掘。[结果/结论]研究发现,基于专利的海洋工程装备产业技术创新网络的链路是可预测的,网络的结构特征与链路预测的实际效果之间存在可量化关联性。 [Purpose/significance]The predictability of network links is the research frontier of complex networks.This paper explores the predictability of technological innovation cooperation network links,which provides support for the prediction and recommendation of technological innovation partners.[Method/process]This paper constructs a technical innovation cooperation network of ocean engineering equipment which based on patents and compare the structural characteristics of this network to some classical complex networks to study the predictability of this network through the theories and methods of complex network.Based on these,this paper explores the relationship between network structure characteristics and the predictability of network links.[Result/conclusion]The technology innovation network links of ocean engineering equipment industry based on patents are predictable.There is a quantifiable correlation between the structural characteristics of the network and the actual effect of link prediction.
作者 汪志兵 韩文民 孙竹梅 潘雪莲 Wang Zhibing
出处 《情报理论与实践》 CSSCI 北大核心 2022年第3期165-172,共8页 Information Studies:Theory & Application
基金 国家自然科学基金青年项目“基于全文本数据的软件实体抽取与学术影响力研究”(项目编号:71704077) 江苏科技大学人文社科项目“海研全球科研项目数据萃智理论应用研究”(项目编号:2017QT018F)的成果。
关键词 可预测性 网络结构特征 中心度 结构洞 技术创新 合作网络 predictability network structure characteristics centrality structural hole technological innovation cooperative network
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  • 1赵爽.产学研合作网络时空演化研究——以中国装备制造业为例[J].现代管理科学,2013,1(11):85-87. 被引量:8
  • 2刘凤朝,刘靓,马荣康.区域间技术交易网络、吸收能力与区域创新产出——基于电子信息和生物医药领域的实证分析[J].科学学研究,2015,33(5):774-781. 被引量:30
  • 3Lti L, Zhou T. Link prediction in complex networks., a surveyl-J~. Physica A, 2011, 390(6)..1150-1170.
  • 4Guimerci R, Sales-Pardo M. Missing and spurious interactions and the reconstruction of complex networks[J~. Proc Natl Acad Sei USA, 2009, 106(52), 22073 - 22078.
  • 5Clauset A, Moore C. Newman M E J. Hierarchical structure and the prediction of missing links in networksEJJ. Nature, 2008,453(7191) .. 98 - 101.
  • 6Lihen-Nowell D, Kleinberg J. The link prediction problem for social networksEC-]//Proceedings of the Twelfth Interna- tional Conference on Information and Knowledge Management. New Orleans, 2003=556 -559.
  • 7Getoor L, Diehl C P. Link mining= a surveyrJ~. ACM SIGKDD Explorations Newsletter, 2005, 7(2):3- 12.
  • 8Sarukkai R R. Link prediction and path analysis using Markov chains[-J~. Computer Networks, 2000, 33(1) ..377 - 386.
  • 9Zhu J, Hong J, Hughes J G. Using Markov chains for link prediction in adaptive web sitesEC~//Proceedings of the In- ternational Conference on Soft-Ware= Computing in an Imperfect World. Berlin, 2002..60- 73.
  • 10Popescul A, Ungar L. Statistical relational learning for link predictionI-C~// Workshop on Learning Statistical Models from Relational Data. New York: ACM Press, 2003.

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