Physica A: Statistical Mechanics and its Applications
Empirical analysis of the evolution of a scientific collaboration network
Section snippets
Introduction and previous work
In recent years, thanks to the increasing availability of machine-readable data, many large networks have been empirically analyzed in detail in several disciplines including communications and information networks, biological, social, and technological networks. In many cases it has been found that these networks have small diameters and high clustering. In other words, any node is relatively close to any other node, and the local connection structure will not be random, but rather shaped by
The frozen collaboration network in 2006
We treat the GP social network as a graph where each node is a GP researcher, i.e. someone who has at least one entry in the bibliography. There is a connection between two people if they have coauthored one or more papers, or if they have coedited at least one book or proceeding. In order to characterize the strength of the interaction, one could attribute a weight to a link with a value proportional, say, to the number of papers coauthored by i and j. However, in the following
Network evolution
In this section we investigate the time evolution of several interesting aspects of the collaboration network.
Testing for preferential attachment
In this section the data is used to test whether the preferential attachment hypothesis (see Section 1) during network growth can be confirmed. As stated in the introduction, preferential attachment has been empirically found in the evolution of several network types, including scientific collaboration networks [3], [4], [5], [10]. However, there are some differences as for the functional form of the effect. In some cases it appears to be quite close to linear, while in other cases it has been
An effective network?
The analysis presented above as well as those of Refs. [3], [4], [5], [10] assume that, once a collaboration between two researchers has been established (a reference to another page in the Wikipedia case), it stays there forever. This seems to be a safe first approximation as scientists remain active for quite a long time in general, at least for the time windows used in Refs. [3], [4], [5]. However, when one looks closely at how these collaborations are established and maintained, one sees
Conclusions
In this work we have studied the temporal evolution of a collaboration network among researchers in the genetic programming field. The distinguishing feature with respect to other similar investigations is that, thanks to the availability of data, we were able to study the network growth from the very beginning and over a 20-year period, which is particularly helpful to watch the formation of the giant component, a process that appears to be still incomplete in the present case. Moreover, the
Acknowledgments
We would like to thank W. B. Langdon, who kindly made the data used in this study available to us. We also thank an anonymous reviewer for his useful comments on the manuscript.
References (25)
- et al.
Evolution of the social network of scientific collaborations
Physica A
(2002) - et al.
Graph structure in the web
Comput. Networks
(2000) - et al.
Scale-free characteristics of random networks: the topology of the World Wide Web
Physica A
(2000) - et al.
A self-consistent approach to measure preferential attachment in networks and its application to an inherent structure network
Physica A
(2007) - et al.
A model for social networks
Physica A
(2006) - et al.
Statistical mechanics of complex networks
Rev. Mod. Phys.
(2002) The structure and function of complex networks
SIAM Rev.
(2003)Clustering and preferential attachment in growing networks
Phys. Rev. E
(2001)- et al.
Measuring preferential attachment in evolving networks
Europhysics Lett.
(2003) - et al.
Empirical analysis of an evolving social network
Science
(2006)
Preferential attachment in the growth of social networks: the Internet encyclopedia Wikipedia
Phys. Rev. E
Emergence of scaling in random networks
Science
Cited by (86)
Evolution of scientific collaboration based on academic ages
2023, Physica A: Statistical Mechanics and its ApplicationsIdentification of research communities of environmental engineering and their evolution using coauthor network analysis
2022, Environmental Modelling and SoftwareNetwork of R packages: A characterization of an empirical collaborative network
2022, Chaos, Solitons and FractalsCitation Excerpt :The most simple model is based on the preferential attachment notion [1,31,32], where a new element attaches to the existing component with higher probability as the element has more connections. The process of creating a new package based on previous ones relates software networks with other innovation networks, including scientific articles [33–37], patent citations [38–41], information sharing communities [42,43], and even precedents in judicial courts [44]. In the following, we present how to obtain information from CRAN and the subsequent construction of the dependency and suggestion networks.
Patent collaborations: From segregation to globalization
2022, Journal of InformetricsPredicting publication productivity for researchers: A piecewise Poisson model
2020, Journal of InformetricsTo what extent is climate change adaptation a novel challenge for agricultural modellers?
2019, Environmental Modelling and SoftwareCitation Excerpt :Key to challenges A and C (Fig. 3), is Collaboration with social scientists with expertise in managing stakeholder engagement (Nguyen et al., 2014; Reed et al., 2014) and particularly those with expertise in normative and critical engagement approaches. However, inter-disciplinary research communities require time, resources, appropriate structures and the application of specific skillsets to flourish (Kipling et al., 2016c; Tomassini and Luthi, 2007). Initiatives such as MACSUR and the Agricultural Model Intercomparison and Improvement Project (AgMIP) (Rosenzweig et al., 2013) have driven progress in agricultural model development and use (Ewert et al., 2015; Sándor et al., 2017) and supported the application of inter-disciplinary expertise to region-specific CC issues (Dono et al., 2016; Özkan Gülzari et al., 2017; Schönhart et al., 2016).