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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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Journal ArticleDOI
TL;DR: A method of determining protein structure networks by calculating inter-residue interaction energies is proposed and it is shown that it gives an accurate and reliable description of the signal-propagation properties of a known allosteric enzyme.
Abstract: Network theory methods are being increasingly applied to proteins to investigate complex biological phenomena. Residues that are important for signaling processes can be identified by their condition as critical nodes in a protein structure network. This analysis involves modeling the protein as a graph in which each residue is represented as a node and edges are drawn between nodes that are deemed connected. In this paper, we show that the results obtained from this type of network analysis (i.e., signaling pathways, key residues for signal transmission, etc.) are profoundly affected by the topology of the network, with normally used determination of network edges by geometrical cutoff schemes giving rise to substantial statistical errors. We propose a method of determining protein structure networks by calculating inter-residue interaction energies and show that it gives an accurate and reliable description of the signal-propagation properties of a known allosteric enzyme. We also show that including co...

67 citations

Journal ArticleDOI
TL;DR: It is proposed that an idealized sampling network should sample high-betweenness stations, small-membership communities which are by definition rare or undersampled relative to other communities, and index stations having large numbers of intracommunity links, while retaining some degree of redundancy to maintain network robustness.
Abstract: . Network theory is applied to an array of streamflow gauges located in the Coast Mountains of British Columbia (BC) and Yukon, Canada. The goal of the analysis is to assess whether insights from this branch of mathematical graph theory can be meaningfully applied to hydrometric data, and, more specifically, whether it may help guide decisions concerning stream gauge placement so that the full complexity of the regional hydrology is efficiently captured. The streamflow data, when represented as a complex network, have a global clustering coefficient and average shortest path length consistent with small-world networks, which are a class of stable and efficient networks common in nature, but the observed degree distribution did not clearly indicate a scale-free network. Stability helps ensure that the network is robust to the loss of nodes; in the context of a streamflow network, stability is interpreted as insensitivity to station removal at random. Community structure is also evident in the streamflow network. A network theoretic community detection algorithm identified separate communities, each of which appears to be defined by the combination of its median seasonal flow regime (pluvial, nival, hybrid, or glacial, which in this region in turn mainly reflects basin elevation) and geographic proximity to other communities (reflecting shared or different daily meteorological forcing). Furthermore, betweenness analyses suggest a handful of key stations which serve as bridges between communities and might be highly valued. We propose that an idealized sampling network should sample high-betweenness stations, small-membership communities which are by definition rare or undersampled relative to other communities, and index stations having large numbers of intracommunity links, while retaining some degree of redundancy to maintain network robustness.

67 citations

Journal ArticleDOI
TL;DR: A new model for the diffusion of information through heterogeneous social networks is discussed, an improvement on earlier ones because it allows a transmitter of information to retain that information after telling it to somebody else.
Abstract: This paper discusses a new model for the diffusion of information through heterogeneous social networks. In earlier models, when information was given by one actor to another the transmitter did not retain the information. The new model is an improvement on earlier ones because it allows a transmitter of information to retain that information after telling it to somebody else. Consequently, the new model allows more actors to have information during the information diffusion process. The model provides predictions of diffusion times in a given network at the global, dyadic, and individual levels. This leads to straightforward generalizations of network measures, such as closeness centrality and betweenness centrality, for research problems that focus on the efficiency of information transfer in a network. We analyze in detail how information diffusion times and centrality measures depend on a series of network measures, such as degrees and bridges. One important finding is that predictions about the time ...

67 citations

Book ChapterDOI
01 Jan 2014
TL;DR: The analytical framework and the results for percolation laws for a network of networks (NON) formed by \(n\) interdependent random networks are reviewed and some possible real-world applications of NON theory are reviewed.
Abstract: Complex networks appear in almost every aspect of science and technology. Previous work in network theory has focused primarily on analyzing single networks that do not interact with other networks, despite the fact that many real-world networks interact with and depend on each other. Very recently an analytical framework for studying the percolation properties of interacting networks has been introduced. Here we review the analytical framework and the results for percolation laws for a network of networks (NON) formed by \(n\) interdependent random networks. The percolation properties of a network of networks differ greatly from those of single isolated networks. In particular, although networks with broad degree distributions, e.g., scale-free networks, are robust when analyzed as single networks, they become vulnerable in a NON. Moreover, because the constituent networks of a NON are connected by node dependencies, a NON is subject to cascading failure. When there is strong interdependent coupling between networks, the percolation transition is discontinuous (is a first-order transition), unlike the well-known continuous second-order transition in single isolated networks. We also review some possible real-world applications of NON theory.

67 citations

Journal ArticleDOI
TL;DR: This paper analyzes the growth and evolution of topological features of the US airline network over a 20-year period, and explores the correlation between different measures, and investigates various interactions inside the network.
Abstract: This paper analyzes the growth and evolution of topological features of the US airline network over a 20-year period. It captures the change in the network system from different dimensions of complex networks such as centrality distribution and various structural properties of the network over time. We first illustrate the results of a set of measures, including degree, strength, betweenness centrality, and clustering structure. The geographic features of airport systems, spatial distance and network efficiency are also discussed in this section. In order to further capture the dynamics of the system, this paper also explores the correlation between different measures, and investigates various interactions inside the network. Overall this study offers a novel approach to understanding the growth and evolution of real physical networks.

66 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202319
202240
202175
2020109
201989
2018115