On time-varying collaboration networks

The patterns of scientific collaboration have been frequently investigated in terms of complex networks without reference to time evolution. In the present work, we derive collaborative networks (from the arXiv repository) parameterized along time. By defining the concept of affine group, we identify several interesting trends in scientific collaboration, including the fact that the average size of the affine groups grows exponentially, while the number of authors increases as a power law. We were therefore able to identify, through extrapolation, the possible date when a single affine group is expected to emerge. Characteristic collaboration patterns were identified for each researcher, and their analysis revealed that larger affine groups tend to be less stable.

[1]  Jiang Wu,et al.  Assessing impact and quality from local dynamics of citation networks , 2012, J. Informetrics.

[2]  Luciano da Fontoura Costa,et al.  Three-feature model to reproduce the topology of citation networks and the effects from authors' visibility on their h-index , 2012, J. Informetrics.

[3]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Sidney Redner,et al.  Community structure of the physical review citation network , 2009, J. Informetrics.

[5]  Nadine Rons.  Partition-based Field Normalization: An approach to highly specialized publication records , 2012, J. Informetrics.

[6]  Wesley Shrum,et al.  Trust, Conflict and Performance in Scientific Collaborations , 2001 .

[7]  HsiuJu Rebecca Yen,et al.  Quantifying the degree of research collaboration: A comparative study of collaborative measures , 2012, J. Informetrics.

[8]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Olle Persson,et al.  Identifying research themes with weighted direct citation links , 2010, J. Informetrics.

[10]  M. Newman.  Coauthorship networks and patterns of scientific collaboration , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[11]  R. Pastor-Satorras,et al.  Activity driven modeling of time varying networks , 2012, Scientific Reports.

[12]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[13]  L. da F. Costa,et al.  Characterization of complex networks: A survey of measurements , 2005, cond-mat/0505185.

[14]  L. Amaral,et al.  The web of human sexual contacts , 2001, Nature.

[15]  Tamara G. Kolda,et al.  Community structure and scale-free collections of Erdös-Rényi graphs , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Piet van Mieghem.  Graph Spectra for Complex Networks: Eigenvalues of the adjacency matrix , 2010 .

[17]  Piet Van Mieghem,et al.  Graph Spectra for Complex Networks , 2010 .

[18]  Luciano da Fontoura Costa,et al.  On the use of topological features and hierarchical characterization for disambiguating names in collaborative networks , 2012, ArXiv.

[19]  D. Lusseau,et al.  The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations , 2003, Behavioral Ecology and Sociobiology.

[20]  Luciano da Fontoura Costa,et al.  Using complex networks concepts to assess approaches for citations in scientific papers , 2012, Scientometrics.

[21]  Ying Ding,et al.  Community detection: Topological vs. topical , 2011, J. Informetrics.