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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23 Dec 2010
TL;DR: A method for analyzing the vulnerability of a power system using network theory, which locates fault by combining the travelling waves methodology with the network topology to isolate the faulty link first and then locate the fault distance.
Abstract: This paper proposes a method for analyzing the vulnerability of a power system using network theory. It locates fault by combining the travelling waves methodology with the network topology to isolate the faulty link first and then locate the fault distance. The algorithm is verified on a test power network using Alternate Transients Program/Electromagnetic Transients Program (ATP/EMTP) and Matlab. The time stamps recorded are combined with the network topology to isolate the faulty link and calculate the fault distance.
8 citations
TL;DR: Methods for treating the interactions in these biological data sets as edges in a network model of the phenotype are examined and relevant network theory methods for analyzing network structure and identifying important genes are reviewed.
Abstract: One of the challenges of understanding the genetic basis of complex phenotypes is explaining variability not attributable to individual genes. While most existing methods that investigate variant mutations or differential gene expression focus on individual effects, a complex system of gene interactions (epistasis) and pathways is likely needed to explain phenotypic variation. Herein, we examine methods for treating the interactions in these biological data sets as edges in a network model of the phenotype and review relevant network theory methods for analyzing network structure and identifying important genes. In particular, we review methods for detecting community structure, describing the statistical properties of networks, and computing network centrality of genes that may reveal insights missed by individual genetic effects. We also discuss available tools to facilitate the construction and visualization of epistasis networks of GWAS data.
8 citations
01 Jan 2013
TL;DR: This paper analyzes dynamic graphs from two different applications, i.e. social science and neuroscience, and proposes a method to efficiently represent the strength of a relation between two entities based on events involving both entities.
Abstract: Dynamic graphs are ubiquitous in real world applications. They can be found, e.g. in biology, neuroscience, computer science, medicine, social networks, the World Wide Web. There is a great necessity and interest in analyzing these dynamic graphs efficiently. Typically, analysis methods from classical data mining and network theory have been studied separately in different fields of research. Dealing with complex networks in real world applications, there is a need to perform interdisciplinary research by combining techniques of different fields. In this paper, we analyze dynamic graphs from two different applications, i.e. social science and neuroscience. We exploit the edge weights in both types of networks to answer distinct questions in the respective fields of science. First, for the representation of edge weights in a social network graph we propose a method to efficiently represent the strength of a relation between two entities based on events involving both entities. Second, we correlate graph measures of electroencephalographic activity networks with clinical variables to find good predictors for patients with visual field damages.
8 citations
TL;DR: Fingerprints of networks are introduced, which are defined as correlation plots of local and global network properties and it is shown that these fingerprints are suitable tools for characterizing networks beyond single-quantity distributions.
Abstract: In complex networks a common task is to identify the most important or “central” nodes There are several definitions, often called centrality measures, which often lead to different results Here, we introduce fingerprints of networks, which we define as correlation plots of local and global network properties We show that these fingerprints are suitable tools for characterizing networks beyond single-quantity distributions In particular, we study the correlations between four local and global measures, namely the degree, the shortest-path betweenness, the random-walk betweenness and the subgraph centrality on different random-network models like Erdős–Renyi, small-world and Barabasi–Albert as well as on different real networks like metabolic pathways, social collaborations and computer networks and compare those fingerprints to determine the quality of those basic models The correlation fingerprints are quite different between the real networks and the model networks questioning whether the models really reflect all important properties of the real world
8 citations