Framework for Explanations of Patent Citation Formation: An Exponential Random Graph Model Perspective

Yang Guancan; Cheng Liang; Zhang Jing; Li Gang

doi:10.13266/j.issn.0252-3116.2019.05.012

Library and Information Service >

2019 , Vol. 63 >Issue 5: 100 - 109

DOI: https://doi.org/10.13266/j.issn.0252-3116.2019.05.012

Framework for Explanations of Patent Citation Formation: An Exponential Random Graph Model Perspective

Yang Guancan ,
Cheng Liang ,
Zhang Jing ,
Li Gang

Expand

1 School of Information Resource Management of Renmin University of China, Beijing 100872;
2 Institute of Science and Technical Information of China, Beijing 100038;
3 School of Information Management of Wuhan University, Hubei 430072

Received date: 2018-07-23

Revised date: 2018-11-15

Online published: 2019-03-05

Fold

Abstract

[Purpose/significance] Although there have been efforts of scholars to answer the question, what’s determinants of patent citation formation are not solved satisfactorily. Scholars find formation of patent citation is influenced by the structure characteristics of patent citation network. However, the current framework of statistical inference methods based on logistical regression is failing to incorporate the above factors, so an innovative method need to be introduced. [Method/process] From a tie formation perspective, patent citation formation represents three broad category of tie formation processes: attribute-based processes, self-organizing network processes and covariates processes. Furthermore, based on these processes, the paper establishes a mapping relationship between those processes with particular types of configurations. Finally, a framework is proposed for understanding the complexity of patent citation formation. [Result/conclusion] The paper introduces a framework for understanding patent citation formation, which lays the groundwork for statistical network modeling in the future. In addition, broadly network configuration selection from the framework offers significant opportunities to extend existing bibliometrics and open new pathways in complexity of scientific network analysis.

Key words： patent citations formation; framework for explanations; statistical network analysis

Cite this article

Yang Guancan , Cheng Liang , Zhang Jing , Li Gang . Framework for Explanations of Patent Citation Formation: An Exponential Random Graph Model Perspective[J]. Library and Information Service, 2019 , 63(5) : 100 -109 . DOI: 10.13266/j.issn.0252-3116.2019.05.012

References

[1] OECD. OECD Patent Statistics Manual[M]. Paris:OECD Publishing, 2009:14-16.
[2] YANG G C, LI G, LI C Y. Using the comprehensive patent citation network (CPC) to evaluate patent value[J]. Scientometrics, 2015, 105(3):1319-1346.
[3] VAN RANN A F J. Patent citations analysis and its value in research evaluation:a review and a new approach to map technology-relevant research[J]. Journal of data and information science, 2017, 2(1):545-38.
[4] SHUMATE M, PALAZZOLO E T. Exponential random graph (p*) models as a method for social network analysis in communication research[J]. Communication methods and measures, 2010, 4(4):341-371.
[5] ROBINS G, PATTISON P, KALISH Y. An introduction to exponential random graph (p*) models for social networks[J]. Social Networks, 2007, 29(2):173-191.
[6] GOODREAU S M, HANDCOCK M S, BUTTS C T. Statnet:software tools for the representation, visualization, analysis and simulation of network data[J]. Journal of statistical software, 2008, 24(1):1-11.
[7] LUSHER D, KOSKINEN J, ROBINS G. Exponential random graph models for social networks[M]. New York:Cambridge University Press, 2012:16-28.
[8] HARRIS J K. An introduction to exponential random graph modeling[M]. London:SAGE Publications, 2013:1-13.
[9] JAFFE A B, DE RASSENFOSSE G. Patent citation data in social science research:overview and best practices[J]. Journal of the Association for Information Science and Technology, 2017, 68(6):1360-1374.
[10] NARIN F, CARPENTER M P, WOOLF P. Technological performance assessments based on patents and patent citations[J]. IEEE Transactions on engineering management, 1984, EM-31(4):172-183.
[11] TRAJTENBERG M. A penny for your quotes:patent citations and the value of innovations[J]. The RAND journal of economics, 1990, 21(1):172-187.
[12] HALL B H, JAFFE A B, TRAJTENBERG M. The NBER patent citation data file:lessons, insights and methodological tools[R]. Cambridge:National Bureau of Economic Research, 2001.
[13] ALCÁCER J, GITTELMAN M, SAMPAT B. Applicant and examiner citations in U.S. patents:an overview and analysis[J]. Research policy, 2009, 38(2):415-427.
[14] ALCÁCER J, GITTELMAN M. Patent citations as a measure of knowledge flows:the influence of examiner citations[J]. Review of economics and statistics, 2006, 88(4):774-779.
[15] JAFFE A B, TRAJTENBERG M. Patents, citations, and innovations:a window on the knowledge economy[M]. New York:MIT Press, 2002:4-19.
[16] CRISCUOLO P, NARULA R, VERSPAGEN B. Role of home and host country innovation systems in R&D internationalisation:a patent citation analysis[J]. Economics of innovation and new technology, 2005, 14(5):417-433.
[17] OWEN-SMITH J, POWELL W W. The expanding role of university patenting in the life sciences:Assessing the importance of experience and connectivity[J]. Research policy, 2003, 32(9):1695-1711.
[18] GUAN J C, GAO X. Exploring the h-index at patent level[J]. Journal of the Association for Information Science and Technology, 2009, 60(1):35-40.
[19] BRUSONI S, CRISCUOLO P, GEUNA A. The knowledge bases of the world's largest pharmaceutical groups:What do patent citations to non-patent literature reveal?[J]. Economics of innovation and new technology, 2005, 14(5):395-415.
[20] CHANG S B, LAI K K, CHANG S M. Exploring technology diffusion and classification of business methods:using the patent citation network[J]. Technological forecasting and social change, 2009, 76(1):107-117.
[21] GRESS B. Properties of the USPTO patent citation network:1963-2002[J]. World patent information, 2010, 32(1):3-21.
[22] STERNITZKE C, BARTKOWSKI A, SCHRAMM R. Visualizing patent statistics by means of social network analysis tools[J]. World patent information, 2008, 30(2):115-131.
[23] HUANG M H, CHIANG L Y, CHEN D Z. Constructing a patent citation map using bibliographic coupling:a study of Taiwan's high-tech companies[J]. Scientometrics, 2003, 58(3):489-506.
[24] EGGHE L, ROUSSEAU R. Co-citation, bibliographic coupling and a characterization of lattice citation networks[J]. Scientometrics, 2002, 55(3):349-361.
[25] 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 Association for Information Science and Technology, 2010, 61(12):2389-2404.
[26] HU X, ROUSSEAU R, CHEN J. On the definition of forward and backward citation generations[J]. Journal of informatics, 2011, 5(1):27-36.
[27] ATALLAH G, RODRÍGUEZ G. Indirect patent citations[J]. Scientometrics, 2013, 67(3):437-465.
[28] HUNG S W, WANG A P. Examining the small world phenomenon in the patent citation network:a case study of the radio frequency identification (RFID) network[J]. Scientometrics, 2009, 82(1):121-134.
[29] BRANTLE T F, FALLAH M H. Complex innovation networks, patent citations and power laws[C]//2007 Portland International Conference on Management of Engineering & Technology. Piscataway:IEEE, 2007:540-549.
[30] SMILKOV D, KOCAREV L. Rich-club and page-club coefficients for directed graphs[J]. Physica A:statistical mechanics and its applications, 2010, 389(11):2290-2299.
[31] LESKOVEC J, KLEINBERG J, FALOUTSOS C. Graphs over time:densification laws, shrinking diameters and possible explanations[C]//Proceeding of the eleventh ACM SIGKDD international conference. New York:ACM, 2005:177-187.
[32] WANG J C, CHIANG C H, LIN S W. Network structure of innovation:can brokerage or closure predict patent quality?[J]. Scientometrics, 2010, 84(3):735-748.
[33] WHITE H D, WELLMAN B, NAZER N. Does citation reflect social structure? Longitudinal evidence from the "Globenet" interdisciplinary research group[J]. Journal of the Association for Information Science and Technology, 2004, 55(2):111-126.
[34] SHIBATA N, KAJIKAWA Y, TAKEDA Y. Comparative study on methods of detecting research fronts using different types of citation[J]. Journal of the Association for Information Science and Technology, 2009, 60(3):571-580.
[35] 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 Association for Information Science and Technology, 2012, 63(7):1313-1326.
[36] THOMPSON P. Patent citations and the geography of knowledge spillovers:evidence from inventor- and examiner-added citations[J]. Review of economics and statistics, 2006, 88(2):383-388.
[37] BOTTAZZI L, PERI G. Innovation and spillovers in regions:evidence from European patent data[J]. European Economic review, 2003, 47(4):687-710.
[38] CHEN D Z, HUANG M H, HSIEH H C. Identifying missing relevant patent citation links by using bibliographic coupling in LED illuminating technology[J]. Journal of informetrics, 2011, 5(3):400-412.
[39] MORRIS S A, VAN DER VEER MARTENS B. Mapping research specialties[J]. Annual review of information science and technology, 2008, 42(1):213-295.
[40] BRAAM R R, MOED H F, VAN RAAN A F J. Mapping of science by combined co-citation and word analysis. I. Structural aspects[J]. Journal of the Association for Information Science and Technology, 1991, 42(4):233-251.
[41] CRISCUOLO P, VERSPAGEN B. Does it matter where patent citations come from? Inventor vs. examiner citations in European patents[J]. Research policy, 2008, 37(10):1892-1908.
[42] BAKKER J. The log-linear relation between patent citations and patent value[J]. Scientometrics, 2016, 110(2):1-14.
[43] LEE Y G, LEE J D, SONG Y I. An in-depth empirical analysis of patent citation counts using zero-inflated count data model:the case of KIST[J]. Scientometrics, 2007, 70(1):27-39.
[44] NICOLAISEN J. Citation analysis[J]. Annual review of information science and technology, 2007, 41(1):609-641.
[45] YOON B, PARK Y. A text mining based patent network:analytical tool for high-technology trend[J]. The journal of high technology management research, 2004, 15(1):37-50.
[46] LEE W J, LEE W K, SOHN S Y. Patent network analysis and quadratic assignment procedures to identify the convergence of robot technologies[J]. PloS One, 2016, 11(10):e0165091.
[47] SNIJDERS T A B, PATTISON P E, ROBINS G L. New specifications for exponential random graph models[J]. Sociological methodology, 2006, 36(1):99-153.
[48] ROSE KIM J Y, HOWARD M, COX PAHNKE E. Understanding network formation in strategy research:exponential random graph models[J]. Strategic management journal, 2016, 37(1):22-44.
[49] ROBINS G. Doing social network research[M]. London:SAGE, 2015:81-85.
[50] MORRIS M, HUNTER D R, HANDCOCK M S. Specification of exponential-family random graph models:terms and computational aspects[J]. Journal of statistical software. 2008, 24(4):1548-7660.
[51] BATAGELJ V. Efficient Algorithms for citation network analysis[EB/OL].[2017-12-06]. https://www.researchgate.net/publication/1956732_Efficient_Algorithms_for_Citation_Network_Analysis.
[52] DERNIS H, HOISL K, WEBB C. Analysing European and international patent citations[EB/OL].[2017-12-06]. http://www.oecd-ilibrary.org/science-and-technology/analysing-european-and-international-patent-citations_883002633010.
[53] MEYER M. What is special about patent citations? Differences between scientific and patent citations[J]. Scientometrics, 2000, 49(1):93-123.
[54] COCKBURN I M, KORTUM S, STERN S. Are all patent examiners equal? The impact of examiner characteristics[R]. Cambridge:National Bureau of Economic Research, 2002.
[55] ZHANG C, BU Y, DING Y. Understanding scientific collaboration:homophily, transitivity, and preferential attachment[J]. Journal of the Association for Information Science and Technology, 2018, 69(1):72-86.
[56] ROBINS G, PATTISON P, WANG P. Closure, connectivity and degree distributions:exponential random graph (p*) models for directed social networks[J]. Social Networks, 2009, 31(2):105-117.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References