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Francisco Acedo, C. Barroso, Cristóbal Rocha, José Galán (2006)
Co-Authorship in Management and Organizational Studies: An Empirical and Network AnalysisWiley-Blackwell: Journal of Management Studies
D. Watts, S. Strogatz (1998)
Collective dynamics of ‘small-world’ networksNature, 393
A. Lotka
The frequency distribution of scientific productivityJournal of Architectural and Planning Research, 15
L. Freeman (1991)
Research Methods in Social Network Analysis
N. Babchuk, B. Keith, G. Peters (1999)
Collaboration in sociology and other scientific disciplines: a comparative trend analysis of scholarship in the socialPhys. Math. Sci. Am. Sociol., 30
Marcus Kaiser (2008)
Mean clustering coefficients: the role of isolated nodes and leafs on clustering measures for small-world networksNew Journal of Physics, 10
J. Moody (2004)
The structure of a social science: disciplinary cohesion from 1963 to 1999Am. Sociol. Rev., 69
F. Yoshikane, K. Kageura (2004)
Comparative analysis of coauthorship networks of different domains: The growth and change of networksScientometrics, 60
Cédric Gossart, Müge Özman (2009)
Co-authorship networks in social sciences: The case of TurkeyScientometrics, 78
P. Marsden (2005)
Models and Methods in Social Network Analysis: Recent Developments in Network Measurement
H. Moed, W. Glänzel, U. Schmoch (2005)
Handbook of Quantitative Science and Technology Research
D.N. Laband, R.D. Tollison (2000)
Intellectual CollaborationJ. Political Econ., 108
P. Doreian, K. Woodard (1992)
Fixed list versus snowball selection of social networksSocial Science Research, 21
M. Gibbons, C. Limoges, H. Nowotny, S. Schwartzman, P. Scott, M. Trow (1994)
The New Production of Knowledge
L. Kronegger (2009)
Clustering of attribute and/or relational dataAdvances in Methodology and Statistics
P.V. Marsden (2005)
Models and Methods in Social Network Analysis
G. Melin, O. Persson (1996)
Studying research collaboration using co-authorshipsScientometrics, 36
John Hudson (1996)
Trends in Multi-authored Papers in EconomicsJournal of Economic Perspectives, 10
J. Katz, B. Martin (1997)
What is research collaborationResearch Policy, 26
F. Palumbo, Carlo Lauro, M. Greenacre (2010)
Data Analysis and Classification
A. Ferligoj, L. Kronegger (2009)
Clustering of attribute and/or relational dataMetodoloski zvezki, 6
James Endersby (1996)
Collaborative research in the social sciences : Multiple authorship and publication creditSocial Science Quarterly, 77
U. Brandes (2008)
On variants of shortest-path betweenness centrality and their generic computationSoc. Networks, 30
L. Hargens (1976)
Patterns of scientific research: a comparative analysis of research in three scientific fieldsSocial Forces, 55
J.W. Endersby (1996)
Collaborative research in the social sciences: multiple authorship and paper creditSoc. Sci. Quart., 77
A.J. Lotka (1926)
The frequency distribution of scientific productivityJ. Wash. Acad. Sci., 16
N. Bakkalbasi, T. Krichel (2007)
Patterns of research collaboration in a digital library for Economics
J. Moody (2004)
The Structure of a Social Science Collaboration Network: Disciplinary Cohesion from 1963 to 1999American Sociological Review, 69
D. Hicks (1999)
The difficulty of achieving full coverage of international social science literature and the bibliometric consequencesScientometrics, 44
R. Yousefi-Nooraie, M. Akbari-Kamrani, R. Hanneman, A. Etemadi (2008)
Association between co-authorship network and scientific productivity and impact indicators in academic medical research centers: A case study in IranHealth Research Policy and Systems, 6
S. Wasserman, Katherine Faust (1994)
Social Network Analysis: Methods and Applications, 8
M. Newman, M. Newman (2000)
Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality.Physical review. E, Statistical, nonlinear, and soft matter physics, 64 1 Pt 2
A. Barabási, Hawoong Jeong, Z. Néda, E. Ravasz, A. Schubert, T. Vicsek (2001)
Evolution of the social network of scientific collaborationsPhysica A-statistical Mechanics and Its Applications, 311
W. Glänzel, A. Schubert (2004)
Analysing Scientific Networks Through Co-Authorship
R. Albert, A. Barabási (2001)
Statistical mechanics of complex networksArXiv, cond-mat/0106096
S. Goyal, Marco Leij, J. Moraga-González (2004)
Economics: An Emerging Small WorldJournal of Political Economy, 114
N. Babchuk, B. Keith, G. Peters (1999)
Collaboration in sociology and other scientific disciplines: A comparative trend analysis of scholarship in the social, physical, and mathematical sciencesThe American Sociologist, 30
L. Freeman (1978)
Centrality in social networks conceptual clarificationSocial Networks, 1
P. Bonacich (1987)
Power and Centrality: A Family of MeasuresAmerican Journal of Sociology, 92
M. Newman (2004)
Coauthorship networks and patterns of scientific collaborationProceedings of the National Academy of Sciences of the United States of America, 101
B. McInnes, Jannene McBride, N. Evans, David Lambert, A. Andrew (1999)
Emergence of Scaling in Random Networks
S. Wasserman, John Scott, P. Carrington (2005)
Models and Methods in Social Network Analysis: Introduction
P. Nicholls (1986)
Empirical validation of Lotka's lawInf. Process. Manag., 22
Scientific collaboration is a complex phenomenon that improves the sharing of competences and the production of new scientific knowledge. Social Network Analysis is often used to describe the scientific collaboration patterns defined by co-authorship relationships. Different phases of the analysis of collaboration are related to: data collection, network boundary setting, relational data matrix definition, data analysis and interpretation of results. The aim of this paper is to point out some issues that arise in these different phases, highlighting: (i) the use of local archives versus international bibliographic databases; (ii) the use of different approaches for setting boundaries in a whole-network; (iii) the definition of a co-authorship data matrix (binary and weighted ties) and (iv) the analysis and the interpretation of network measures for co-authorship data. We discuss the different choices that can be made in these phases within an illustrative example on real data which is referred to scientific collaboration among researchers affiliated to an academic institution. In particular, we compare global and actor-level network measures computed from binary and weighted co-authorship networks in different disciplines.
Quality & Quantity – Springer Journals
Published: Apr 2, 2011
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