Zeng A, Shen Z, Zhou J, Wu J, Fan Y, Wang Y, Stanley HE (2017) The science of science: from the perspective of complex systems. Phys Rep 714–715:1–73

Article
MathSciNet
MATH
Google Scholar

Fortunato S, Bergstrom CT, Börner K, Evans JA, Helbing D, Milojević S, Petersen AM, Radicchi F, Sinatra R, Uzzi B et al. (2018) Science of science. Science 359(6379):0185

Article
Google Scholar

Sinatra R, Deville P, Szell M, Wang D, Barabási A-L (2015) A century of physics. Nat Phys 11(10):791

Article
Google Scholar

Battiston F, Musciotto F, Wang D, Barabási A-L, Szell M, Sinatra R (2019) Taking census of physics. Nat Rev Phys 1(1):89

Article
Google Scholar

Hoonlor A, Szymanski BK, Zaki MJ (2013) Trends in computer science research. Commun ACM 56(10):74–83

Article
Google Scholar

Cheng Q, Lu X, Liu Z, Huang J (2015) Mining research trends with anomaly detection models: the case of social computing research. Scientometrics 103(2):453–469

Article
Google Scholar

Effendy S, Yap RHC (2017) Analysing trends in computer science research: a preliminary study using the Microsoft academic graph. In: WWW ’17 companion proceedings of the 26th international conference on World Wide Web companion, pp 1245–1250

Chapter
Google Scholar

Della Briotta Parolo P, Pan RK, Ghosh R, Huberman BA, Kaski K, Fortunato S (2015) Attention decay in science. J Informetr 9(4):734–745

Article
Google Scholar

Yin Y, Wang D (2017) The time dimension of science: connecting the past to the future. J Informetr 11(2):608–621

Article
Google Scholar

Pan RK, Petersen AM, Pammolli F, Fortunato S (2018) The memory of science: inflation, myopia, and the knowledge network. J Informetr 12(3):656–678

Article
Google Scholar

Wagner CS, Roessner JD, Bobb K, Klein JT, Boyack KW, Keyton J, Rafols I, Börner K (2011) Approaches to understanding and measuring interdisciplinary scientific research (IDR): a review of the literature. J Informetr 5(1):14–26

Article
Google Scholar

Leydesdorff L, Rafols I (2011) Indicators of the interdisciplinarity of journals: diversity, centrality, and citations. J Informetr 5(1):87–100

Article
Google Scholar

Bromham L, Dinnage R, Hua XX (2016) Interdisciplinary research has consistently lower funding success. Nature 534(7609):684–687

Article
Google Scholar

Leydesdorff L, Wagner CS, Bornmann L (2019) Interdisciplinarity as diversity in citation patterns among journals: Rao–Stirling diversity, relative variety, and the Gini coefficient. J Informetr 13(1):255–269

Article
Google Scholar

Franzoni C, Scellato G, Stephan P (2014) The mover’s advantage: the superior performance of migrant scientists. Econ Lett 122(1):89–93

Article
Google Scholar

Deville P, Wang D, Sinatra R, Song C, Blondel VD, Barabási AL (2015) Career on the move: geography, stratification, and scientific impact. Sci Rep 4(1):4770

Article
Google Scholar

Scellato G, Franzoni C, Stephan PE (2015) Migrant scientists and international networks. Res Policy 44(1):108–120

Article
Google Scholar

Foster JG, Rzhetsky A, Evans JA (2015) Tradition and innovation in scientists’ research strategies. Am Sociol Rev 80(5):875–908

Article
Google Scholar

Jia T, Wang D, Szymanski BK (2017) Quantifying patterns of research-interest evolution. Nat Hum Behav 1(4):78

Article
Google Scholar

Arrieta OAD, Pammolli F, Petersen AM (2017) Quantifying the negative impact of brain drain on the integration of European science. Sci Adv 3(4):e1602232

Article
Google Scholar

Vaccario G, Verginer L, Schweitzer F (2018) Reproducing scientists’ mobility: a data-driven model. arXiv preprint. arXiv:1811.07229

James C, Pappalardo L, Sirbu A, Simini F (2018) Prediction of next career moves from scientific profiles. arXiv preprint. arXiv:1802.04830

Wuchty S, Jones BF, Uzzi B (2007) The increasing dominance of teams in production of knowledge. Science 316(5827):1036–1039

Article
Google Scholar

Bettencourt LMA, Kaiser DI, Kaur J (2009) Scientific discovery and topological transitions in collaboration networks. J Informetr 3(3):210–221

Article
Google Scholar

Milojević S (2014) Principles of scientific research team formation and evolution. Proc Natl Acad Sci USA 111(11):3984–3989

Article
Google Scholar

Petersen AM (2015) Quantifying the impact of weak, strong, and super ties in scientific careers. Proc Natl Acad Sci USA 112(34):201501444

Article
Google Scholar

Larivière V, Gingras Y, Sugimoto CR, Tsou A (2015) Team size matters: collaboration and scientific impact since 1900. J Assoc Inf Sci Technol 66(7):1323–1332

Article
Google Scholar

Zeng XHT, Duch J, Sales-Pardo M, Moreira JAG, Radicchi F, Ribeiro HV, Woodruff TK, Amaral LAN (2016) Differences in collaboration patterns across discipline, career stage, and gender. PLoS Biol 14(11):e1002573

Article
Google Scholar

Czaika M, Orazbayev S (2018) The globalisation of scientific mobility, 1970–2014. Appl Geogr 96:1–10

Article
Google Scholar

Hall KL, Vogel AL, Huang GC, Serrano KJ, Rice EL, Tsakraklides SP, Fiore SM (2018) The science of team science: a review of the empirical evidence and research gaps on collaboration in science. Am Psychol 73(4):532–548

Article
Google Scholar

Bu Y, Murray DS, Ding Y, Huang Y, Zhao Y (2018) Measuring the stability of scientific collaboration. Scientometrics 114(2):463–479

Article
Google Scholar

Abramo G, D’Angelo CA, Costa FD (2019) The collaboration behavior of top scientists. Scientometrics 118(1):215–232

Article
Google Scholar

Yu S, Bedru HD, Lee I, Xia F (2019) Science of scientific team science: a survey. Comput Sci Rev 31:72–83

Article
Google Scholar

Wagner CS, Whetsell TA, Mukherjee S (2019) International research collaboration: novelty, conventionality, and atypicality in knowledge recombination. Res Policy 48(5):1260–1270

Article
Google Scholar

Petersen AM, Riccaboni M, Stanley HE, Pammolli F (2012) Persistence and uncertainty in the academic career. Proc Natl Acad Sci USA 109(14):5213–5218

Article
Google Scholar

Penner O, Pan RK, Petersen AM, Kaski K, Fortunato S (2013) On the predictability of future impact in science. Sci Rep 3(1):3052

Article
Google Scholar

Petersen AM, Fortunato S, Pan RK, Kaski K, Penner OB, Rungi A, Riccaboni M, Stanley HE, Pammolli F (2014) Reputation and impact in academic careers. Proc Natl Acad Sci USA 111(43):15316–15321

Article
Google Scholar

Wang D, Song C, Barabási A-L (2013) Quantifying long-term scientific impact. Science 342(6154):127–132

Article
Google Scholar

Sinatra R, Wang D, Deville P, Song C, Barabási A-L (2016) Quantifying the evolution of individual scientific impact. Science 354(6312):5239

Article
Google Scholar

Clauset A, Larremore DB, Sinatra R (2017) Data-driven predictions in the science of science. Science 355(6324):477–480

Article
Google Scholar

Veugelers R, Wang J (2019) Scientific novelty and technological impact. Res Policy 48(6):1362–1372

Article
Google Scholar

Börner K, Chen C, Boyack KW (2003) Visualizing knowledge domains. Annu Rev Inf Sci Technol 37(1):179–255

Article
Google Scholar

Boyack KW, Klavans R, Börner K (2005) Mapping the backbone of science. Scientometrics 64(3):351–374

Article
Google Scholar

Leydesdorff L, Rafols I (2009) A global map of science based on the isi subject categories. J Am Soc Inf Sci Technol 60(2):348–362

Article
Google Scholar

Rafols I, Porter AL, Leydesdorff L (2010) Science overlay maps: a new tool for research policy and library management. J Am Soc Inf Sci Technol 61(9):1871–1887

Article
Google Scholar

Bollen J, Van de Sompel H, Hagberg A, Bettencourt L, Chute R, Rodriguez MA, Balakireva L (2009) Clickstream data yields high-resolution maps of science. PLoS ONE 4(3):4803

Article
Google Scholar

Guevara MR, Hartmann D, Aristarán M, Mendoza M, Hidalgo CA (2016) The research space: using career paths to predict the evolution of the research output of individuals, institutions, and nations. Scientometrics 109(3):1695–1709

Article
Google Scholar

Bengio Y, Ducharme R, Vincent P, Jauvin C (2003) A neural probabilistic language model. J Mach Learn Res 3:1137–1155

MATH
Google Scholar

Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Burges CJC, Bottou L, Welling M, Ghahramani Z, Weinberger KQ (eds) Advances in neural information processing systems, vol 26. Curran Associates, Inc., New York, pp 3111–3119

Google Scholar

Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint. arXiv:1301.3781

Huang EH, Socher R, Manning CD, Ng AY (2012) Improving word representations via global context and multiple word prototypes. In: Proceedings of the 50th annual meeting of the association for computational linguistics: long papers, vol 1. Association for Computational Linguistics, Stroudsburg, pp 873–882

Google Scholar

Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

Chapter
Google Scholar

Bojanowski P, Grave E, Joulin A, Mikolov T (2017) Enriching word vectors with subword information. Trans Assoc Comput Linguist 5:135–146

Article
Google Scholar

Wu L, Fisch A, Chopra S, Adams K, Bordes A, Weston J (2017) Starspace: embed all the things! arXiv preprint. arXiv:1709.03856

Salton G, Wong A, Yang C-S (1975) A vector space model for automatic indexing. Commun ACM 18(11):613–620

Article
MATH
Google Scholar

Small H (1973) Co-citation in the scientific literature: a new measure of the relationship between two documents. J Am Soc Inf Sci 24(4):265–269

Article
MathSciNet
Google Scholar

Hidalgo CA, Klinger B, Barabási A-L, Hausmann R (2007) The product space conditions the development of nations. Science 317(5837):482–487

Article
Google Scholar

Hidalgo CA, Balland P-A, Boschma R, Delgado M, Feldman M, Frenken K, Glaeser E, He C, Kogler DF, Morrison A et al. (2018) The principle of relatedness. In: International conference on complex systems. Springer, Cham, pp 451–457

Google Scholar

AIP-Publishing: PACS 2010 Regular Edition (2010). https://publishing.aip.org/publishing/pacs/pacs-2010-regular-edition. Accessed 2017-08-03

Zhang Q, Perra N, Gonçalves B, Ciulla F, Vespignani A (2013) Characterizing scientific production and consumption in physics. Sci Rep 3:1640

Article
Google Scholar

Balassa B (1965) Trade liberalisation and “revealed” comparative advantage. Manch Sch 33(2):99–123

Article
Google Scholar

Aquino A (1981) Changes over time in the pattern of comparative advantage in manufactured goods: an empirical analysis for the period 1962–1974. Eur Econ Rev 15(1):41–62

Article
Google Scholar

Soete LG, Wyatt SM (1983) The use of foreign patenting as an internationally comparable science and technology output indicator. Scientometrics 5(1):31–54

Article
Google Scholar

Crafts NF, Thomas M (1986) Comparative advantage in uk manufacturing trade, 1910–1935. Econ J 96(383):629–645

Article
Google Scholar

Van Hulst N, Mulder R, Soete LL (1991) Exports and technology in manufacturing industry. Weltwirtsch Arch 127(2):246–264

Article
Google Scholar

Cantwell J (1995) The globalisation of technology: what remains of the product cycle model? Camb J Econ 19(1):155–174

Google Scholar

Amiti M (1999) Specialization patterns in Europe. Weltwirtsch Arch 135(4):573–593

Article
Google Scholar

Iapadre PL (2001) Measuring international specialization. Int Adv Econ Res 7(2):173–183

Article
Google Scholar

De Benedictis L, Gallegati M, Tamberi M (2008) Semiparametric analysis of the specialization-income relationship. Appl Econ Lett 15(4):301–306

Article
Google Scholar

OECD (2011) Globalisation, comparative advantage and the changing dynamics of trade. OECD Publishing, Paris

Book
Google Scholar

Amighini A, Leone M, Rabellotti R (2011) Persistence versus change in the international specialization pattern of Italy: how much does the ‘district effect’ matter? Reg Stud 45(3):381–401

Article
Google Scholar

D’Agostino LM, Laursen K, Santangelo GD (2013) The impact of R&D offshoring on the home knowledge production of OECD investing regions. J Econ Geogr 13(1):145–175

Article
Google Scholar

Liegsalz J, Wagner S (2013) Patent examination at the state intellectual property office in China. Res Policy 42(2):552–563

Article
Google Scholar

Bahar D, Hausmann R, Hidalgo CA (2014) Neighbors and the evolution of the comparative advantage of nations: evidence of international knowledge diffusion? J Int Econ 92(1):111–123

Article
Google Scholar

Freund C, Pierola MD (2015) Export superstars. Rev Econ Stat 97(5):1023–1032

Article
Google Scholar

Boschma R, Frenken K, Bathelt H, Feldman M, Kogler D et al. (2012) Technological relatedness and regional branching. In: Beyond territory. Dynamic geographies of knowledge creation, diffusion and innovation, pp 64–68

Google Scholar

Neffke F, Henning M, Boschma R (2011) How do regions diversify over time? Industry relatedness and the development of new growth paths in regions. Econ Geogr 87(3):237–265

Article
Google Scholar

Kogler DF, Rigby DL, Tucker I (2013) Mapping knowledge space and technological relatedness in US cities. Eur Plan Stud 21(9):1374–1391

Article
Google Scholar

Boschma R, Minondo A, Navarro M (2013) The emergence of new industries at the regional level in Spain: a proximity approach based on product relatedness. Econ Geogr 89(1):29–51

Article
Google Scholar

Boschma R, Heimeriks G, Balland P-A (2014) Scientific knowledge dynamics and relatedness in biotech cities. Res Policy 43(1):107–114

Article
Google Scholar

Boschma R, Balland P-A, Kogler DF (2014) Relatedness and technological change in cities: the rise and fall of technological knowledge in us metropolitan areas from 1981 to 2010. Ind Corp Change 24(1):223–250

Article
Google Scholar

Essletzbichler J (2015) Relatedness, industrial branching and technological cohesion in us metropolitan areas. Reg Stud 49(5):752–766

Article
Google Scholar

Rigby DL (2015) Technological relatedness and knowledge space: entry and exit of us cities from patent classes. Reg Stud 49(11):1922–1937

Article
Google Scholar

Boschma R (2005) Proximity and innovation: a critical assessment. Reg Stud 39(1):61–74

Article
Google Scholar

Boschma R, Frenken K (2010) The spatial evolution of innovation networks: a proximity perspective. In: The handbook of evolutionary economic geography. Edward Elgar, Cheltenham Glos

Chapter
Google Scholar

Cohen WM, Levinthal DA (2000) Absorptive capacity: a new perspective on learning and innovation. In: Strategic learning in a knowledge economy. Elsevier, Amsterdam, pp 39–67

Chapter
Google Scholar

World Bank: world development indicators (2019). http://datatopics.worldbank.org/world-development-indicators/. Accessed 2019-04-04

Tacchella A, Cristelli M, Caldarelli G, Gabrielli A, Pietronero L (2012) A new metrics for countries’ fitness and products’ complexity. Sci Rep 2:723

Article
MATH
Google Scholar

Cristelli M, Tacchella A, Cader M, Roster K, Pietronero L (2017) On the predictability of growth. Policy Research working paper (WPS8117)

Hamilton WL, Leskovec J, Jurafsky D (2016) Diachronic word embeddings reveal statistical laws of semantic change. arXiv preprint. arXiv:1605.09096

Szymanski T (2017) Temporal word analogies: identifying lexical replacement with diachronic word embeddings. In: Proceedings of the 55th annual meeting of the association for computational linguistics (volume 2: short papers), vol 2, pp 448–453

Chapter
Google Scholar

Conneau A, Lample G, Ranzato M, Denoyer L, Jégou H (2017) Word translation without parallel data. arXiv preprint. arXiv:1710.04087

Lample G, Conneau A, Denoyer L, Ranzato M (2017) Unsupervised machine translation using monolingual corpora only. arXiv preprint. arXiv:1711.00043

Radicchi F, Fortunato S, Markines B, Vespignani A (2009) Diffusion of scientific credits and the ranking of scientists. Phys Rev E 80(5):056103

Article
Google Scholar

Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159

MathSciNet
MATH
Google Scholar

Frome A, Corrado GS, Shlens J, Bengio S, Dean J, Ranzato M, Mikolov T (2013) Devise: a deep visual-semantic embedding model. In: Advances in neural information processing systems, vol 26, pp 2121–2129

Google Scholar