1. Business School, University of Shanghai for Science and Technology, Shanghai 200093, China; 2. School of Eeconomics and Management, Qingdao University of Science and Technology, Qingdao 266061, China
Abstract：We propose a model to quantify long-term scientific impact and obtain analytic results of the model. It is more reasonable than the model proposed by Barabási et al. The total citation count of a paper in its life cycle represents its long-term scientific impact. The results show that the value is only related to the paper’s fitness. It means that the content and the quality of the paper represents, the capability of its competitiveness, and determine its long-term impact.
索琪, 郭进利. 评价长期科学影响的模型[J]. 复杂系统与复杂性科学, 2016, 13(1): 64-67.
SUO Qi, GUO Jinli. A Model to Quantify Long-term Scientific Impact[J]. Complex Systems and Complexity Science, 2016, 13(1): 64-67.
[1] Garfield E. Citation analysis as a tool in journal evaluation[J].Science, 1972, (178):471-479. [2] Yu P, Van de Sompel H. Networks of scientific papers[J].Science, 1965, 169: 510-515. [3] Price D S. A general theory of bibliometric and other cumulative advantage processes[J].Journal of the American Society for Information Science, 1976, 27(5): 292-306. [4] Barabási A L, Albert R. Emergence of scaling in random networks[J].Science, 1999, 286 (5439): 509-512. [5] Redner S. How popular is your paper? An empirical study of the citation distribution[J].The European Physical Journal B-Condensed Matter and Complex Systems, 1998, 4(2): 131-134. [6] Bianconi G, Barabási A L. Competition and multiscaling in evolving networks[J].Europhysics Letters, 2001, 54(4): 436. [7] Caldarelli G, Capocci A, De Los Rios P, et al. Scale-free networks from varying vertex intrinsic fitness[J].Physical Review Letters, 2002, 89(25): 258702. [8] Servedio V D P, Caldarelli G, Butta P. Vertex intrinsic fitness: how to produce arbitrary scale-free networks[J].Physical Review E, 2004, 70(5): 056126. [9] Amaral L A N, Scala A, Barthelemy M, et al. Classes of small-world networks[J].Proceedings of the National Academy of Sciences, 2000, 97(21): 11149-11152. [10] Zhu H, Wang X, Zhu J Y. Effect of aging on network structure[J].Physical Review E, 2003, 68(5): 056121. [11] Hajra K B, Sen P. Aging in citation networks[J].Physica A, 2005, 346(1): 44-48. [12] Hajra K B, Sen P. Modelling aging characteristics in citation networks[J].Physica A, 2006, 368(2): 575-582. [13] Medo M, Cimini G, Gualdi S. Temporal effects in the growth of networks[J].Physical Review Letters, 2011, 107(23): 238701. [14] Wang M, Yu G, Yu D. Effect of the age of papers on the preferential attachment in citation networks[J].Physica A, 2009, 388(19): 4273-4276. [15] Wang D, Song C, Barabási A L. Quantifying long-term scientific impact[J].Science, 2013, 342(6154): 127-132. [16] Guo J L, Suo Q. Comment on “quantifying long-term scientific impact”[DB/OL].[2014-05-15].http://arXiv.org/abs/1405.1574v2, 2014. [17] Blumm N, Ghoshal G, Forró Z, et al. Dynamics of ranking processes in complex systems[J].Physical Review Letters, 2012, 109(12): 128701.
