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Many investigations of scientific collaboration are based on statistical analyses of large networks constructed from bibliographic repositories. These investigations often rely on a wealth of bibliographic data, but very little or no other information about the individuals in the network, and thus, fail to illustrate the broader social and academic landscape in which collaboration takes place. In this article, we perform an in-depth longitudinal analysis of a relatively small network of scientific collaboration (N = 291) constructed from the bibliographic record of a research centerin the development and application of wireless and sensor network technologies. We perform a preliminary analysis of selected structural properties of the network, computing its range, configuration and topology. We then support our preliminary statistical analysis with an in-depth temporal investigation of the assortative mixing of selected node characteristics, unveiling the researchers’ propensity to collaborate preferentially with others with a similar academic profile. Our qualitative analysis of mixing patterns offers clues as to the nature of the scientific community being modeled in relation to its organizational, disciplinary, institutional, and international arrangements of collaboration.

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Syndrome ” section), we conduct an in-depth analysis of a single scientific community. In the second part (“ A stochastic model of scientific creativity ” section), we develop and test a stochastic model of network evolution. The case study is

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89 94 Seeley, D.A. 2000. Network evolution and the emergence of structure. In: T.R.J. Bossomaier and D.G. Green (eds.), Complex Systems. Cambridge University Press, Cambridge

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. Coevolution and the architecture of mutualistic networks . Evolution 67 : 338 – 354 . Okuyama , T. and Holland , J.N. 2008 . Network structural properties mediate the stability of mutualistic

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): Manufacturing Network Evolution: A Manufacturing Plant Perspective . International Journal of Operations & Production Management , 31 ( 12 ): 1311 – 1331 . Cohen , W. M. – Levinthal , D

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Authors: Zaida Chinchilla-Rodríguez, Anuska Ferligoj, Sandra Miguel, Luka Kronegger, and Félix de Moya-Anegón

peripherial cluster (these two researchers do not collaborate with any other researcher). Fig. 2 Dynamics of evolving networks. Evolution of the DHUBI blockmodel structure: a blockmodel structure and b co

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to its refusal of accepting a new node, network evolution can only happen on the edges, which include the emergence of new edges and changing of edge weights. The link number is denoted by the degree distribution, whereas the link strength is

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. R. S. Thorpe 1984 Coding morphometric characters for constructing distance Wagner networks Evolution 38 244 255

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