Topic
Network theory
About: Network theory is a research topic. Over the lifetime, 2257 publications have been published within this topic receiving 109864 citations.
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TL;DR: In this paper, the authors apply network theory for the first time to the economically and socially important area of sport and examine relationships between National Governing Bodies (NGBs), media and corporate sponsors.
47 citations
01 Oct 2017
TL;DR: The first application of multivariate visibility graphs to fMRI data is presented, and some relevant aspects of its application to BOLD time series are described, and the analogies and differences with existing methods are discussed.
Abstract: Visibility algorithms are a family of methods that map time series into graphs, such that the tools of graph theory and network science can be used for the characterization of time series. This approach has proved a convenient tool, and visibility graphs have found applications across several disciplines. Recently, an approach has been proposed to extend this framework to multivariate time series, allowing a novel way to describe collective dynamics. Here we test their application to fMRI time series, following two main motivations, namely that (a) this approach allows vs to simultaneously capture and process relevant aspects of both local and global dynamics in an easy and intuitive way, and (b) this provides a suggestive bridge between time series and network theory that nicely fits the consolidating field of network neuroscience. Our application to a large open dataset reveals differences in the similarities of temporal networks (and thus in correlated dynamics) across resting-state networks, and gives indications that some differences in brain activity connected to psychiatric disorders could be picked up by this approach.
47 citations
TL;DR: DACCER is simple, yet efficient, in assessing node centrality while allowing a distributed implementation that contributes to its performance, which contributes to the practical applicability of DACCER to the analysis of large complex networks.
47 citations
TL;DR: Extensions to closeness, betweenness and eigenvector centrality are explored and the extension to betweenness suggests a linkage to the Gould and Fernandez brokerage measures, which are explored.
46 citations
TL;DR: It is shown that an inequality for eigenvalues of Erdos-Renyi random graphs can be derived from an induced subgraph of it based on interlacing theorems and this work decomposes the natural connectivity of a network as localnatural connectivity of its connected components.
Abstract: In network theory, a complex network represents a system whose evolving structure and dynamic behavior contribute to its robustness. The natural connectivity is recently proposed as a spectral measure to characterize the robustness of complex networks. We decompose the natural connectivity of a network as local natural connectivity of its connected components and quantify their contributions to the network robustness. In addition, we compare the natural connectivity of a network with that of an induced subgraph of it based on interlacing theorems. As an application, we derive an inequality for eigenvalues of Erdos-Renyi random graphs.
46 citations