Evolution of the social network of scientific collaborations

By Albert-László Barabási, Hawoong Jeong, Z. Néda, E. Ravasz, A. Schubert, Tamás Vicsek. 2001

2,781 citations recorded by the source corpus.

The co-authorship network of scientists represents a prototype of complex evolving networks. In addition, it offers one of the most extensive database to date on social networks. By mapping the electronic database containing all relevant journals in mathematics and neuro-science for an 8-year period (1991–98), we infer the dynamic and the structural mechanisms that govern the evolution and topology of this complex system. Three complementary approaches allow us to obtain a detailed characterization. First, empirical measurements allow us to uncover the topological measures that characterize the network at a given moment, as well as the time evolution of these quantities. The results indicate that the network is scale-free, and that the network evolution is governed by preferential attachment, affecting both internal and external links. However, in contrast with most model predictions the average degree increases in time, and the node separation decreases. Second, we propose a simple model that captures the network's time evolution. In some limits the model can be solved analytically, predicting a two-regime scaling in agreement with the measurements. Third, numerical simulations are used to uncover the behavior of quantities that could not be predicted analytically. The combined numerical and analytical results underline the important role internal links play in determining the observed scaling behavior and network topology. The results and methodologies developed in the context of the co-authorship network could be useful for a systematic study of other complex evolving networks as well, such as the world wide web, Internet, or other social networks.

Subfields: Information Systems, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/31cdc8842f46fa0433897bba4386ecb570afe5b4

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Citations in the corpus

Statistical physics of social dynamics (2007, background): “Recently the interest of physicists triggered the investigation of many different networks: from the network of scientific collaborations (Barabási et al., 2002; Newman, 2001a,b, 2004) to that of sexual contacts (Liljeros et al., 2001) and the ongoing social relationships (Holme, 2003), from email…”
Is prokaryotic complexity limited by accelerated growth inregulatory overhead? (2003, background): “1 differs from the growth behavior described for previously studied metabolic and man-made networks, which largely feature non-accelerating growth and stationary statistics [6, 8, 9, 19, 20].”
White matter maturation reshapes structural connectivity in the late developing human brain (2010, background): “Empirical studies have examined evolving social networks from scientific collaborations (4) and the time evolution of Internet dating communities (5).”
Flink: Semantic Web technology for the extraction and analysis of social networks (2005, background): “[6,2] using commercially available databases or by looking for other kinds of evidence of co-participation in research activities, such as public information about project grants[10,8].”
Coauthorship networks and patterns of scientific collaboration (2004, neutral): “scientific collaboration”
Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. (2000, background): “There has, in fact, been a substantial surge of interest in social networks within the physics community recently, as evidenced by the large body of papers on the topic—see Refs.”
The topology of dark networks (2008, background): “We expected that the first two mechanisms would generate a powerlaw degree distribution(1) and that the third would generate a high clustering coefficient and two-regime scaling behavior.(2) Our simulations showed that the power -law degree distributions are easily regenerated, with R(2) ranging fro”
Patterns and dynamics of users' behavior and interaction: Network analysis of an online community (2009, background): “However, because, in our case, the system follows a non-linear process of “accelerated” growth, the structure of ties becomes denser over time, and thus average separation between users decreases (Ahn et al., 2007; Barabási et al., 2002; Holme et al., 2004; Leskovec, Kleinberg, & Faloutsos, 2005).”
Power law distributions in entrepreneurship: : Implications for theory and research (2015, background): “In so doing, our study extends a growing recognition that the social world seems to be organized according to power law distributions (Aguinis and O'Boyle, 2014; Aguinis et al., in press; Andriani and McKelvey, 2007, 2009; Barabási et al., 2002; Boisot and McKelvey, 2010; Powell, 2003).”