Evolution and Structure of the Internet: A Statistical Physics Approach
By D. Bawden. J. Documentation, 2005
392 citations recorded by the source corpus.
The talk will present an overview of the large-scale topological and dynamical properties of real Internet maps. First I will review the methodology used to obtain large scale maps of the Internet at the router and autonomous system level. Then I will discuss the statistical features and regularities observed in the large scale structure of the Internet and the importance of the dynamics in the formulation of adequate models. Finally the various results and models will be scrutinized in the light of a statistical theory that considers the map’s incompleteness due to measurement biases.
Subfields: Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/1a5b0a15093d4f3341ea339adf5d2477f28f67f1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistical and Nonlinear Physics
- Mark Newman
- Marc Barthelemy
- Santo Fortunato
- Alessandro Vespignani
- Albert-László Barabási
- Olaf Sporns
- Matjaz Perc
- M. A. Porter
Citations in the corpus
- Towards a Theory of Scale-Free Graphs: Definition, Properties, and Implications (2005, background): “An accessible history of the influence of criticality in the SF literature can found in [13, pp. 73-78].”
- The effects of spatial constraints on the evolution of weighted complex networks (2005, background): “The empirical evidence coming from studies on systems belonging to areas as diverse as social sciences, biology and computer science have shown that the usual paradigm of random graphs is often not well suited to describe real world networks [1, 2, 3, 4].”
- Prominence and control: the weighted rich-club effect. (2008, methodology): “In this Letter, we adopt the framework of network theory – where the elements of the system are seen as nodes and the links among the elements represent interactions [4, 5, 6, 7, 8] – and provide a novel method to properly assess this tendency.”
- Velocity and hierarchical spread of epidemic outbreaks in scale-free networks. (2003, background): “[5] R.”
- Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network (2013, methodology): “Economics also made use of the concepts of network science, gaining additional insight to the more traditional approach [8–13].”
- The "robust yet fragile" nature of the Internet. (2005, background): “The deeper origins of the claims involving power laws and hubs arise from the SF models’ roots in statistical physics, in which any particular graph is interpreted as an element from a larger statistical ensemble of graphs, with probability weights that typically arise either implicitly through some”
- Modeling the evolution of weighted networks. (2004, background): “, in a range of values usually observed in the empirical analysis of networked structures [1,3,4].”
- Catastrophic cascade of failures in interdependent networks (2009, background): “After a decade of intense study on networks, almost all work done has concentrated on the limited case of a single network which does not interact with other networks [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12].”
- Finding Statistically Significant Communities in Networks (2010, neutral): “Finding statistically significant communities in networks Andrea Lancichinetti,1, 2 Filippo Radicchi,3 José J. Ramasco,4, 1 and Santo Fortunato1 1Complex Networks & Systems Lagrange Laboratory, ISI Foundation, Turin, Italy 2Department of Physics, Politecnico di Torino, Turin, Italy 3Amaral Lab,…”
- Temporal Networks (2011, background): “These happen globally a few times per minute, but compared to the size of the entire Internet, this is so slow that one can probably safely assume it is static [58,59].”
- Theory of Rumour Spreading in Complex Social Networks (2007, neutral): “[30]): Pðk0jkÞ 1⁄4 qðk0Þ 1⁄4 k 0Pðk0Þ hki , (18)”
- Fast exact shortest-path distance queries on large networks by pruned landmark labeling (2013, background): “Other applications of distance queries include top-k keyword queries on linked data [16,37], discovery of optimal pathways between compounds in metabolic networks [31, 32], and management of resources in computer networks [28,7].”
- Structure and Dynamics of Networks (2009, background): “One of the most obvious and prominent examples is the Internet [Albert et al., 1999, Barabási & Albert, 1999, Pastor-Satorras & Vespignani, 2004].”