Análisis de influencia de la red de colaboración de opciones reales

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Hernandes Coutinho Fagundes Rodrigo Tavares Nogueira


La teoría de opciones reales surgió como una alternativa para valorar las flexibilidades arraigadas en proyectos y ha adquirido popularidad desde finales del siglo xx. A través de métodos bibliométricos y teoría de grafos, este documento crea un análisis de la red de colaboración compuesta por los investigadores de opciones reales, que incluye trabajos científicos de dieciocho años. En este esfuerzo identificamos meticulosamente a los autores y sus alianzas de coautoría, encontrando una topología distinta sin un componente gigante. Al desarrollar modelos no ponderados y ponderados, la red se desenreda y proporciona mediciones a partir de la propensión a la internacionalización y el cálculo de diferentes métricas de impacto, que reconocen a los investigadores más relevantes sobre el tema.

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Archambault, É., Campbell, D., Gingras, Y., & Larivière, V. (2009). Comparing Bibliometric Statistics Obtained from the Web of Science and Scopus. Journal of the Association for Information Science and Technology, 60(7), 1320-1326.

Barabási, A.-L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509-512.

Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the Social Network of Scientific Collaborations. Physica A: Statistical Mechanics and its Applications, 311(3), 590-614.

Bar-Ilan, J. (2008). Which H-index? - A Comparison of WoS, Scopus and Google Scholar. Scientometrics, 74(2), 257-271.

Bar-Ilan, J., Levene, M., & Lin, A. (2007). Some Measures for Comparing Citation Databases. Journal of Informetrics, 1(1), 26-34.

Barrat, A., Barthelemy, M., & Vespignani, A. (2007). The Architecture of Complex Weighted Networks: Measurements and Models. In Large Scale Structure and Dynamics of Complex Networks: From Information Technology to Finance and Natural Science (pp. 67-92): World Scientific.

Bartneck, C., & Kokkelmans, S. (2011). Detecting h-index Manipulation Through Self-Citation Analysis. Scientometrics, 87(1), 85-98.

Bastian, M., Heymann, S., & Jacomy, M. Gephi: An Open Source Software for Exploring and Manipulating Networks. In International AAAI Conference on Weblogs and Social Media. Association for the Advancement of Artificial Intelligence, 2009.

Borgatti, S. P. (2005). Centrality and Network Flow. Social Networks, 27(1), 55-71.

Borgatti, S. P., Carley, K. M., & Krackhardt, D. (2006). On the Robustness of Centrality Measures under Conditions of Imperfect Data. Social Networks, 28(2), 124-136.

Cervantes, E. P., & Mena-Chalco, J. P. (2010). A New Approach to Detect Communities in Multi-Weighted Co-Authorship Networks. In 2010 xxix International Conference of the Chilean Computer Science Society, Antofagasta, Chile, 15-19 Nov. 2010 2010 (pp. 131-138): IEEE.

Cervantes, E. P., Mena-Chalco, J. P., & Cesar, R. M. (2012). Towards a Quantitative Academic Internationalization Assessment of Brazilian Research Groups. In 2012 IEEE 8th International Conference on E-Science, Chicago, IL, USA, 8-12 Oct. 2012 (pp. 1-8): IEEE.

Cervantes, E. P., Mena-Chalco, J. P., De Oliveira, M. C. F., & Cesar, R. M. (2013). Using Link Prediction to Estimate the Collaborative Influence of Researchers. In 2013 IEEE 9th International Conference on eScience, Beijing, China, 22-25 Oct. 2013 2013 (pp. 293-300): IEEE.
Clauset, A., Shalizi, C. R., & Newman, M. E. (2009). Power-law Distributions in Empirical Data. SIAM Review, 51(4), 661-703.

Colizza, V., Flammini, A., Serrano, M. A., & Vespignani, A. (2006). Detecting Rich-Club Ordering in Complex Networks. Nature physics, 2(2), 110-115.

Dixit, A. K., & Pindyck, R. S. (1994). Investment Under Uncertainty. Princeton: Princeton University Press.

Dixit, A. K., & Pindyck, R. S. (1995). The Options Approach to Capital Investment. Harvard Business Review, 73(3), 105-115.

Elsevier (2014). Scopus Quick Reference Guide. Available at:

Elsevier (2018a). Elsevier Developers. Available at

Elsevier (2018b). Scopus. Available at:

Fagundes, H. C., & Nogueira, R. T. (2017). Analyzing the Collaboration Network of Real Options Authors. Paper presented at the 21st Annual International Conference on Real Options, Boston.

Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, Web of Science, and Google Scholar: Strengths and Weaknesses. The faseb journal, 22(2), 338-342.

Flaatten, H., Rasmussen, L. S., & Haney, M. (2016). Publication Footprints and Pitfalls of Bibliometry. Acta Anaesthesiologica Scandinavica, 60(1), 3-5.

Freeman, L. C. (1978). Centrality in Social Networks Conceptual Clarification. Social Networks, 1(3), 215-239.

Fruchterman, T. M., & Reingold, E. M. (1991). Graph Drawing by Force-Directed Placement. Software: Practice and experience, 21(11), 1129-1164.

GitHub (2017). R Package to Interface with Elsevier and Scopus APIs. Available at:

Groos, O. V., & Pritchard, A. (1969). Documentation Notes. Journal of Documentation, 25(4), 344-349.

Haak, L. L., Fenner, M., Paglione, L., Pentz, E., & Ratner, H. (2012). ORCID: a System to Uniquely Identify Researchers. Learned Publishing, 25(4), 259-264.

Hirsch, J. E. (2005). An Index to Quantify an Individual’s Scientific Research Output. Proceedings of the National academy of Sciences of the United States of America, 102(46), 16569.

Hou, H., Kretschmer, H., & Liu, Z. (2007). The Structure of Scientific Collaboration Networks in Scientometrics. Scientometrics, 75(2), 189-202.

Latapy, M. (2008). Main-Memory Triangle Computations for Very Large (Sparse [Power-Law]) Graphs. Theoretical Computer Science, 407(1-3), 458-473.

Leite, P., Mugnaini, R., & Leta, J. (2011). A New Indicator for International Visibility: Exploring Brazilian Scientific Community. Scientometrics, 88(1), 311.

Luthi, L., Tomassini, M., Giacobini, M., & Langdon, W. B. The Genetic Programming Collaboration Network and its Communities. In Proceedings of the 9th annual conference on Genetic and Evolutionary Computation, 2007 (pp. 1643-1650): ACM.

Merton, R. K. (1968). The Matthew Effect in Science. Science, 159(3810), 56-63.

Newman, M. E. (2001). Scientific Collaboration Networks. II. Shortest Paths, Weighted Networks and Centrality. Physical Review E, 64(1), 016132.

Newman, M. E. (2004). Coauthorship Networks and Patterns of Scientific Collaboration. Proceedings of the national academy of sciences, 101(suppl 1), 5200-5205.

Newman, M. E. (2009). Random Graphs with Clustering. Physical review letters, 103(5), 058701.

Newman, M. E., Watts, D. J., & Strogatz, S. H. (2002). Random Graph Models of Social Networks. Proceedings of the national academy of sciences, 99(suppl. 1), 2566-2572.

Opsahl, T. (2010). Closeness Centrality in Networks with Disconnected Components. Available at:

Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node Centrality in Weighted Networks: Generalizing Degree and Shortest Paths. Social Networks, 32(3), 245-251.

Otte, E., & Rousseau, R. (2002). Social Network Analysis: A Powerful Strategy, also for the Information Sciences. Journal of information Science, 28(6), 441-453.

Tomassini, M., & Luthi, L. (2007). Empirical Analysis of the Evolution of a Scientific Collaboration Network. Physica A: Statistical Mechanics and its Applications, 385(2), 750-764.

Trigeorgis, L. (1996). Real Options: Managerial Flexibility and Strategy in Resource Allocation. Cambridge (MA): MIT Press.

van Steen, M. (2010). Graph Theory and Complex Networks - An Introduction (vol. 144). United States: van Steen, Maarten.


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