Scale-free characteristics of random networks: the topology of the world-wide web

By Albert-László Barabási, Réka Albert, Hawoong Jeong. 2000

1,457 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/de9d9903b0a1766c52d4ddc654f7d5cd62ba7244

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Trees, networks, and hydrology (2006, background): “Many recent contributions have addressed the related features of observed network structures both natural and artificial, like, e.g., the Internet [Barabási et al., 2000; Yook et al., 2001], cellular and metabolic networks [e.g., Barabási, 2002], food webs [Montoya and Solé, 2002; Garlaschelli et”
A Random Graph Model for Power Law Graphs (2001, methodology): “For example, [6, 7 ] implicitly assume that the probability that a node has a given degree is a continuous function.”
The ecological consequences of complex topology and nested structure in pollination webs. (2006, background): “It is interesting that, confirming expectations of models for abiotic networks (Albert and Barabasi 2002; Albert et al. 2000; Barabasi and Albert 1999; Barabasi et al. 2000; Jeong et al. 2000), KAT1, a scale-free network was less robust to the loss of plant species.”
Directed scale-free graphs (2003, background): “1 Introduction Recently many new random graph models have been introduced and analyzed, inspired by certain common features observed in many large-scale real-world graphs such as the 'web graph', whose vertices are web pages (or sites), with a directed edge for each link between two web pages.”
The phase transition in inhomogeneous random graphs (2005, methodology): “For example, considering the scale-free model of Barabási and Albert [9], the diameter was determined heuristically and experimentally to be Θ(log n) in [4, 10, 81]; for a precise version of this model, the LCD model, the value (1 + o(1)) log n/ log log n was found rigorously in [23]; later, this va”
Characterization of complex networks: A survey of measurements (2005, background): “of network topology. Supported by the availability of high performance computers and large data collections, results like the discovery of the scale-free structure of the Internet [18] and of the WWW [19, 20] were of major importance for the increased interest on the new area of complex networks, wh”
Mathematical results on scale‐free random graphs (2005, methodology): “Computer experiments presented by Barabási, Albert and Jeong [3, 7] and heuristic arguments given by Newman, Strogatz and Watts [48] suggest that G (n) m should have diameter of the form A + B log n.”
Synchronization in scale-free dynamical networks: robustness and fragility (2001, background): “In the case of the World Wide Web (WWW), the nodes are Web sites and they are joined when there is a hyper-link from one to the other [2-6].”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, methodology): “In another experiment, random versions of observed data that preserve the original degree distribution were used to estimate the average geodesic length 〈d〉 of the entire WWW [10, 248].”
A random graph model for massive graphs (2000, methodology): “Several other papers have taken a different approach to modeling power law graphs than the one taken here [2; 5; 6; 10; 12].”
The “New” Science of Networks (2004, background): “…2002, Montoya & Sole 2002), transportation networks (Sen networks of boards of directors (Davis et al. 2003) and scientists (Newman 2001), the Internet (Faloutsos et al. 1999), and the link structure of the World Wide Web (Adamic 1999, Albert et al. 1999, Barabasi et al. 2000, Broder et al. 2000).”
Deeper Inside PageRank (2004, background): “Barabasi et al. [10, 11, 52] has uncovered the scale-free structure of the Web.”
Social network analysis of animal behaviour : a promising tool for the study of sociality (2008, background): “Dolphin social groups showed a ‘scale-free’ pattern (meaning that the degree distribution of the network follows a power law: Barabási & Albert 1999), giving it similar properties (such as robustness to the random removal of individuals) of other scale-free networks, like the Internet or molecular ”
White matter maturation reshapes structural connectivity in the late developing human brain (2010, background): “For example, the emergence of scale-free networks can be explained by a preferential attachment rule (1, 2), with important functional consequences (3).”
Effect of sampling on topology predictions of protein-protein interaction networks (2005, methodology): “These partial networks are then used to infer the topology of the whole networ”