A statistical physics perspective on Web growth
Paul L. Krapivsky,Sidney Redner +1 more
TLDR
Approaches from statistical physics are applied to investigate the structure of network models whose growth rules mimic aspects of the evolution of the World Wide Web and obtain distinct power-law forms for the in-degree and out-degree distributions with exponents that are in good agreement with current data for the web.About:
This article is published in Computer Networks.The article was published on 2002-06-21 and is currently open access. It has received 85 citations till now. The article focuses on the topics: Degree distribution & Scale-free network.read more
Citations
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Journal ArticleDOI
The Structure and Function of Complex Networks
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Journal ArticleDOI
Complex networks: Structure and dynamics
TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.
Journal ArticleDOI
Meeting Strangers and Friends of Friends: How Random are Social Networks?
TL;DR: It is shown that as the random/network-based meeting ratio varies, the resulting degree distributions can be ordered in the sense of stochastic dominance, which allows us to infer how the formation process affects average utility in the network.
Journal ArticleDOI
Growing network with local rules: preferential attachment, clustering hierarchy, and degree correlations.
TL;DR: It is claimed that an effective linear preferential attachment is the natural outcome of growing network models based on local rules and that the local models offer an explanation for other properties like the clustering hierarchy and degree correlations recently observed in complex networks.
Book ChapterDOI
The Economics of Social Networks
TL;DR: The aim is to provide some perspective on the research from these literatures, with a focus on the formal modeling of social networks and the two major types of models: Those based on random graphs and those based on game theoretic reasoning.
References
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Journal ArticleDOI
Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Journal ArticleDOI
Statistical mechanics of complex networks
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Book
Introduction to percolation theory
Dietrich Stauffer,Amnon Aharony +1 more
TL;DR: In this paper, a scaling solution for the Bethe lattice is proposed for cluster numbers and a scaling assumption for cluster number scaling assumptions for cluster radius and fractal dimension is proposed.
Journal ArticleDOI
Exploring complex networks
TL;DR: This work aims to understand how an enormous network of interacting dynamical systems — be they neurons, power stations or lasers — will behave collectively, given their individual dynamics and coupling architecture.