Understanding Mechanisms of Patent Citation Formation Based on ERGM: A Case Study of the Nelarabine Drug

  • Yang Guancan ,
  • Liu Zhanlin ,
  • Li Gang
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  • 1. School of Information Resource Management of Renmin University of China, Beijing 1000872;
    2. Department of Industrial and Systems Engineering, University of Washington, Seattle 98105;
    3. School of Information Management of Wuhan University, Wuhan 430072

Received date: 2018-08-05

  Revised date: 2018-11-27

  Online published: 2019-05-20

Abstract

[Purpose/significance] The Formation of patent citation is necessary to understand innovation networks. The independence assumption set by the Conventional regression model for observed objects cannot integrate the structural effect factors of the network into the model to provide comprehensive statistical inference. ERGMs (exponential random graph model) represent a methodological innovation of statistical inference for networks given their ability to model actor attributes along with endogenous self-organizational processes and exogenous network covariates.[Method/process] In this paper, ERGMs are applied to systematic inspect the five mechanisms affecting patent citation formation in a sample of Nelarabine drug. The five mechanisms contain main effect, difference effect of citation lag, and activity effect, transitivity effect and network covariates.[Result/conclusion] We find that five different types of mechanisms play diverse roles in patent citation formation. And three of effects among these mechanisms have significant impacts on citation formation of nelarabine drug:network covariates based on shared inventors and shared patent family membership, and transitivity effect. In addition, some aided mechanism play a supporting role on patent citation formation, such as difference of time lag, main effects of number of claims and reference.

Cite this article

Yang Guancan , Liu Zhanlin , Li Gang . Understanding Mechanisms of Patent Citation Formation Based on ERGM: A Case Study of the Nelarabine Drug[J]. Library and Information Service, 2019 , 63(10) : 75 -86 . DOI: 10.13266/j.issn.0252-3116.2019.10.009

References

[1] OECD. OECD patent statistics manual[M]. Paris:OECD Publishing, 2009.
[2] 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.
[3] 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.
[4] VAN RAAN 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):545-538.
[5] MORRIS S A, VAN DER VEER MARTENS B. Mapping research specialties[J]. Annual review of information science and technology, 2008, 42(1):213-295.
[6] ARRIETA PAREDES M P, CRONIN B. Exponential random graph models for management research:a case study of executive recruitment[J]. European management journal, 2017, 35(3):373-382.
[7] 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.
[8] 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.
[9] 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.
[10] JAFFE A B, TRAJTENBERG M. Patents, citations, and innovations[M]. New York:MIT Press, 2002.
[11] 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.
[12] ROBINS G. Doing social network research[M]. London:SAGE, 2015.
[13] FISCHER T, LEIDINGER J. Testing patent value indicators on directly observed patent value-an empirical analysis of Ocean Tomo patent auctions[J]. Research policy, 2014, 43(3):519-529.
[14] 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.
[15] 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.
[16] BENSON C L, MAGEE C L. Quantitative determination of technological improvement from patent data[J]. Public library of science, 2015, 10(4):e0121635.
[17] CZARNITZKI D, HUSSINGER K, SCHNEIDER C. "Wacky" patents meet economic indicators[J]. Economics letters, 2011, 113(2):131-134.
[18] 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.
[19] BRANTLE T F, FALLAH M H. Complex innovation networks, patent citations and power laws[C]//PICMET'07-2007 Portland international conference on management of engineering & technology. Portland:IEEE,2007:540-549.
[20] WANG J C, CHIANG C H, LIN S W. Network structure of innovation:can brokerage or closure predict patent quality?[J]. Scientometrics, springer netherlands, 2010, 84(3):735-748.
[21] BATAGELJ V. Efficient algorithms for citation network analysis[EB/OL].[2017-12-31]. https://arxiv.org/abs/cs/0309023.pdf.
[22] 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.
[23] ALMEIDA P, KOGUT B. The exploration of technological diversity and geographic localization in innovation:start-up firms in the semiconductor industry[J]. Small business economics, 1997, 9(1):21-31.
[24] 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.
[25] 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.
[26] 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.
[27] ROBINS G, SNIJDERS T, WANG P. Recent developments in exponential random graph (p*) models for social networks[J]. Social networks, 2007, 29(2):192-215.
[28] THORNE N, AULD D S, INGLESE J. Apparent activity in high-throughput screening:origins of compound-dependent assay interference[J]. Current opinion in chemical biology, 2010, 14(3):315-324.
[29] LIM S Y, SUH M. Intellectual property business models using patent acquisition:a case study of royalty pharma inc[J]. Journal of commercial biotechnology, 2016, 22(2):6-18.
[30] WAGNER S, WAKEMAN S. What do patent-based measures tell us about product commercialization? evidence from the pharmaceutical industry[J]. Research policy, 2016, 45(5):1091-1102.
[31] KISOR D F. Collaboration to meet a therapeutic need:the development of nelarabine[J/OL]. Clinical nedicine. 2009, 1:1317-1320.[2018-12-06]. https://dio.org/10.4137/CMT.s2909.
[32] FDA approval for nelarabine[EB/OL].[2017-12-06]. https://www.cancer.gov/about-cancer/treatment/drugs/fda-nelarabine.
[33] COHEN M H, JOHNSON J R, JUSTICE R. FDA drug approval summary:nelarabine (Arranon) for the treatment of T-cell lymphoblastic leukemia/lymphoma[J]. The oncologist, 2008, 13(6):709-714.
[34] KADIA T M, GANDHI V. Nelarabine in the treatment of pediatric and adult patients with T-cell acute lymphoblastic leukemia and lymphoma[J]. Expert review of hematology, 2016, 10(1):1-8.
[35] PAPADATOS G, DAVIES M, DEDMAN N. SureChEMBL:a large-scale, chemically annotated patent document database[J]. Nucleic acids research, 2016, 44(D1):D1220-D1228.
[36] MARRA M, EMROUZNEJAD A, HO W. The value of indirect ties in citation networks:SNA analysis with OWA operator weights[J]. Information sciences, 2015, 314:135-151.
[37] LUKE D. A user's guide to network analysis in R[M]. Cham:Springer International Publishing, 2015.
[38] DUBNJAKOVIC A. An evaluation of exponential random graph modeling and its use in library and information science studies[J]. Library & information science research, 2016, 38(3):259-264.
[39] 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.
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