Scale-free networks from varying vertex intrinsic fitness.

By Guido Caldarelli, Andrea Capocci, P. D. L. Rios, Miguel Angel Muñoz. Physical Review Letters, 2002

703 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/43b9712e295e1922bc325737da66e86ccd38cf13

How researchers see this paper

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

In Molecular Biology

1. Mark Newman
2. Albert-László Barabási
3. Matjaz Perc
4. Dmitri V. Krioukov
5. Marián Boguñá
6. Santo Fortunato
7. José Fernando Ferreira Mendes
8. Sergey N. Dorogovtsev

In Statistical and Nonlinear Physics

1. Mark Newman
2. Albert-László Barabási
3. Matjaz Perc
4. José Fernando Ferreira Mendes
5. Sergey N. Dorogovtsev
6. Marc Barthelemy
7. Alexander V. Goltsev
8. R. V. Solé

Citations in the corpus

• Quantifying Long-Term Scientific Impact (2013, background): “ee Fig. 1C and SOM S2.1) ! !!=! 1 2!! !! exp− ln!!−!! ! ! 2!! (1) C) Fitness, ! !, captures the inherent differences between papers, accounting for the perceived novelty and importance of a discovery (24, 25). Novelty and importance depend on so many intangible and subjective dimensions that it is i”
• Statistical mechanics of networks. (2004, result): “The classical limit of this model has been studied previously by a number of other authors [17, 29, 30, 31, 32], although again developed and justified in a different way from our presentation here; generally the edge probability (47) has been taken as an assumption, rather than a derived result.”
• Random Dot Product Graph Models for Social Networks (2007, background): “[12, 13] and formalized by Kraetzl, Nickel, Scheinerman, and Tucker [1, 2].”
• Protein-to-protein interactions: Technologies, databases, and algorithms (2010, background): “For these aims, a number of papers [Caldarelli et al. 2002; Deeds et al. 2006] tried to model with re­spect to the underlying biochemistry of protein interactions.”
• The Structure and Function of Complex Networks (2003, background): “[78], and also gives power-law degree distributions under some circumstances.”
• Popularity versus similarity in growing networks (2011, background): “Yet wider and more general class of models, to which our approach actually belongs, are the network models with hidden variables [11, 50].”