scispace - formally typeset
Open AccessJournal ArticleDOI

Complex networks: Structure and dynamics

Reads0
Chats0
TLDR
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.
About
This article is published in Physics Reports.The article was published on 2006-02-01 and is currently open access. It has received 9441 citations till now. The article focuses on the topics: Network dynamics & Complex network.

read more

Figures
Citations
More filters
Journal ArticleDOI

Complex brain networks: graph theoretical analysis of structural and functional systems

TL;DR: This article reviews studies investigating complex brain networks in diverse experimental modalities and provides an accessible introduction to the basic principles of graph theory and highlights the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
Journal ArticleDOI

Complex network measures of brain connectivity: uses and interpretations.

TL;DR: Construction of brain networks from connectivity data is discussed and the most commonly used network measures of structural and functional connectivity are described, which variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, and test resilience of networks to insult.
Journal ArticleDOI

Community detection in graphs

TL;DR: A thorough exposition of community structure, or clustering, is attempted, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists.
Journal ArticleDOI

Community detection in graphs

TL;DR: A thorough exposition of the main elements of the clustering problem can be found in this paper, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
References
More filters
Journal Article

Random Walks on Complex Networks

TL;DR: In this paper, the authors investigate random walks on complex networks and derive an exact expression for the mean firstpassage time (MFPT) between two nodes, where the centrality of a node determines the relative speed by which a node can receive and spread information over the network.
Journal ArticleDOI

Heterogeneity in Oscillator Networks: Are Smaller Worlds Easier to Synchronize?

TL;DR: It is found that networks with a homogeneous distribution of connectivity are more synchronizable than heterogeneous ones, even though the average network distance is larger.
Journal ArticleDOI

The elusive concept of brain connectivity.

TL;DR: Until it is understood what each definition means in terms of an underlying neural substrate, comparisons of functional and/or effective connectivity across studies may appear inconsistent and should be performed with great caution.
Journal ArticleDOI

The Human Organization

TL;DR: It is proposed that effective knowledge‐based businesses will be built on human network connections, which requires much greater investment in social processes of integration and in the individual ability to connect with each other.
Journal ArticleDOI

Communication with Chaotic Lasers

TL;DR: The experimental demonstration of chaotic communication with an optical system is described, using an erbium-doped fiber ring laser to produce chaotic light and embedded in the larger chaotic carrier and transmitted to a receiver system where the message was recovered from the chaos.
Frequently Asked Questions (1)
Q1. What are the contributions in "Complex networks: structure and dynamics" ?

The authors review the major concepts and results recently achieved in the study of the structure and dynamics of complex networks, and summarize the relevant applications of these ideas in many different disciplines, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.