Competition and multiscaling in evolving networks

By Ginestra Bianconi, Albert-László Barabási. 2000

1,006 citations recorded by the source corpus.

The rate at which nodes in a network increase their connectivity depends on their fitness to compete for links. For example, in social networks some individuals acquire more social links than others, or on the www some webpages attract considerably more links than others. We find that this competition for links translates into multiscaling, i.e. a fitness-dependent dynamic exponent, allowing fitter nodes to overcome the more connected but less fit ones. Uncovering this fitter-gets-richer phenomenon can help us understand in quantitative terms the evolution of many competitive systems in nature and society.

Subfields: Genetics, Sociology and Political Science, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Sociology and Political Science

In Statistical and Nonlinear Physics

Citations in the corpus

Analysis of topological characteristics of huge online social networking services (2007, background): “In any fitness-based approach, each node has its own fitness value and they are linked by the function of their fitness values [24, 25, 26].”
Brokerage in SME networks (2010, background): “Recent research has shown that knowl- Policy 39 (2010) 375–385 edge does not circulate freely among local actors (Bianconi and Barabási, 2001; Verspagen and Werker, 2004).”
Evolution of networks (2001, background): “r linear preference functions. In general, the probability for a new link to be attached to a vertex sat time tis p(s,t) = G(s,t)k(s,t) + A(s,t). The coeﬃcient G(s,t) may be called ﬁtness of a vertex [184,181] A(s,t) is additional attractiveness. As we have seen, Acan change the values of the expone”
Business fluctuations and bankruptcy avalanches in an evolving network economy (2009, background): “See Bernanke et al. (1999) for a comprehensive exposition of the approach.”
The Structure and Function of Complex Networks (2003, methodology): “Then in Section VII we look at models of growing networks, particularly the models of Price [344] and Barabási and Albert [32], and generalizations.”
Strong and weak tests of macroecological theory (2003, background): “There are also deep CLT-like statistical reasons for why we might find a power distribution ( Simon 1955, Mandelbrot 1963, Bak et al. 1987, West and Shlesinger 1989, Dewdney 2000, Allen et al. 2001, Bianconi and Barabasi 2001 ).”
The role of venture capital firms in Silicon Valley's complex innovation network (2009, background): “@It thus appears that understanding Silicon Valley's complexity and the hidden functions of VC firms can help policy-makers who try to create innovative clusters. Future comparative research would do well to consider how the success of such new clusters is related to the extent and significance of v”