[Purpose/significance] With the complexity of the scientific system, interdisciplinary research has become an important paradigm and inevitable trend of modern scientific innovation research. Identifying interdisciplinary relevant knowledge combination that has high cooperation potential, becomes the key to promoting interdisciplinary scientific research cooperation and innovation. [Method/process] Firstly,the paper selected target subject source literature,its interdisciplinary reference literature, and its interdisciplinary citing literature, so as to construct an interdisciplinary knowledge weak reference relational network based on keywords. Secondly, it classified the types of Knowledge Medium b, and identified the weak relational structure of the Knowledge Node a of the target discipline-Knowledge Medium b - Interdisciplinary Knowledge c. Finally, the paper defined the Knowledge Node Influence Index AI of the target discipline, the Knowledge Media Influence Index BI, the Interdisciplinary Knowledge Correlation Index CI, the Interdisciplinary Knowledge Combination a-c and Potential Cooperation Index P, to identify the interdisciplinary knowledge combination with high cooperation potential. [Result/conclusion] The papers of 9 CSSCI journals in the field of informatics from 2015 to 2019 and their interdisciplinary references and citations were selected as samples for empirical research, to verify the effectiveness and feasibility of the discovery method of interdisciplinary knowledge combination based on weak citation relationship, and to identify the interdisciplinary knowledge combination with high cooperation potential of intelligence disciplines such as "scientific research cooperation"-"knowledge flow"-"population dynamics model".
[1] GATES A J, KE Q, VAROL O, et al. Nature's reach:narrow work has broad impact[J]. Nature, 2019, 575(7781):32-34.
[2] GRANOVETTER M. The strength of weak ties[J]. The American journal of sociology,1973, 78(6):1360-1380.
[3] ONNELA J P, SARAMAKAI J, HYVONEN J, et al. Structure and tie strengths in mobile communication networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(18):7332-7336.
[4] BAKSHY E, ROSENN I, MARLOW C, et al. The role of social networks in information diffusion[C]/Proceedings of the 21st annual conference on World Wide Web.Lyon:Annual conference on World Wide Web,2012:519-528.
[5] ZHAO J, WU J, XU K. Weak ties:subtle role of information diffusion in online social networks[J]. Physical review E,2010, 82(1):16105.
[6] DAVID E, JON K. Networks, crowds, and markets:reasoning about a highly connected world[M]. United Kingdom:Cambridge University Press, 2012.
[7] GENIUS S K. Published literature and diffusion of medical innovation:exploring innovationgeneration[J]. The Canadian journal of information and library science,2005, 29(1):27-54.
[8] ABBASI A, ALTMANNl J, HOSSAIN L. Identifying the effects of co-authorship networks on the performance of scholars:a correlation and regression analysis of performance measure and social network analysis measures[J]. Journal of informetrics, 2011, 5(4):594-607.
[9] BETTONI M, SCHILLER G, BERNHARD W. Weak ties cooperation in the CoRe knowledge network[C]//The 9th European conference on knowledge management. Southampton:Southampton Solent University,2008:1-7.
[10] YANG L Y, MORRIS S. Mapping institutions and their weak ties in a specialty:a case stud y of cystic fibrosis body composition research[J]. Scientometrics, 2009, 79(2):421-434.
[11] 隗玲,许海云,郭婷,等. 基于弱共现和突发监测的情报学学科研究主题及交叉性分析[J]. 图书情报工作,2015, 59(21):105-114.
[12] 李长玲,刘小慧,刘运梅,等. 基于开放式非相关知识发现的潜在跨学科合作研究主题识别——以情报学与计算机科学为例[J]. 情报理论与实践,2018, 41(2):100-104.
[13] 刘小慧,李长玲,崔斌,等. 基于闭合式非相关知识发现的潜在跨学科合作研究主题识别——以情报学与计算机科学为例[J]. 情报理论与实践, 2017, 40(9):71-76.
[14] SONG M, KANG K, YOUNG A. Investigating drug-disease interactions in drug-symptom-disease triples via citation relations[J]. Journal of the Association for Information Science and Technology, 2018, 69(11):1355-1368.
[15] 梁镇涛,巴志超,徐健. 基于引文的跨学科领域发展路径分析——以眼动追踪领域为例[J]. 图书情报工作, 2019, 63(23):65-78.
[16] PORTER A L, COHEN A S, ROESSNER D J, et al. Measuring researcher interdisciplinarity[J]. Scientometrics, 2007, 72(1):117-147.
[17] 林向义,罗洪云,李秀成. 企业个体从社交媒体网络吸收异质性知识的过程机理:弱连接关系视角[J]. 情报理论与实践, 2019, 42(3):65-71.
[18] 杜德慧,李长玲,相富钟,等. 基于引文关键词的跨学科相关知识发现方法探讨[J/OL].情报杂志:1-6[2020-09-21].http://kns.cnki.net/kcms/detail/61.1167.G3.20200721.1527.004.html.
[19] 张瑞,赵栋祥,唐旭丽,等. 知识流动视角下学术名词的跨学科迁移与发展研究[J]. 情报理论与实践,2020, 43(1):47-55.
[20] LOUADI M,MOHAMED El. Knowledge heterogeneity and social network analysis-towards conceptual and measurement clarifications[J]. Knowledge management research & practice, 2008, 6(3):199-213.
[21] 王雪,李睿. 知识生态学视角下的各类引文现象阐释[J]. 情报杂志, 2018, 37(9):179-184
[22] FAJARDO-ORTIZ D, LOPEZ-CERVANTES, DURAN L, et al. The emergence and evolution of the research fronts in HIV/AIDS research[J]. Plos one, 2017, 12(5):e178293.
[23] 周复恭, 黄运成. 应用线性回归分析[M]. 北京:中国人民大学出版社, 1989.
[24] 李峰,刘静延,蒋录全. 预测方法的发展及最新动态[J]. 情报杂志, 2005(6):76-77.
[25] 牛顿. 自然哲学的数学原理[M].赵振江,译.北京:商务印书馆, 2009.
[26] 冯志刚,李长玲,刘小慧,等. 基于引用与被引用文献信息的图书情报学跨学科性分析[J]. 情报科学,2018, 36(3):105-111.
[27] 李长玲,牌艳欣,相富钟,等. 改进z指数的高被引学科研究热点识别方法探讨[J]. 情报理论与实践,2020(6):39-45,66.
[28] 李月琳,章小童,王姗姗,等. 情报学的坚守与拓展——基于2018年ASIS&T年会论文的综述[J]. 图书情报知识, 2019(3):4-16.
[29] 刘运梅,李长玲,杜德慧. 时间序列视角下PLOS ALM指标特性识别模型构建与应用[J]. 情报资料工作, 2019, 40(6):6-15.
[30] 宗张建. 睡美人文献识别方法研究进展[J]. 图书情报工作, 2019, 63(16):132-142.
[31] 吕晓赞,王晖,周萍. 中美大数据论文的跨学科性比较研究[J]. 科研管理, 2019, 40(4):1-13.
[32] 李国杰,程学旗. 大数据研究:未来科技及经济社会发展的重大战略领域——大数据的研究现状与科学思考[J]. 中国科学院院刊,2012, 27(6):647-657.
[33] 李纲,巴志超. 科研合作超网络下的知识扩散演化模型研究[J]. 情报学报, 2017, 36(3):274-284.
[34] KIVAN V, LEWIS M, BENTZ B J, et al. A dynamical model for bark beetle outbreaks[J]. Journal of theoretical biology, 2016, 407(21):25-37.
[35] 陈文杰,许海云. 一种基于多元数据融合的引文网络知识表示方法[J]. 情报理论与实践, 2020, 43(1):150-154.
[36] BREKELMANS A M, POORT H W, SLOOFF J H. A new method to analyse the mechanical behaviour of skeletal parts[J]. Acta orthopaedica scandinavica, 2009, 43(5):301-317.
