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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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01 Mar 2014
TL;DR: A new technique is presented that efficiently finds the k most central entities in terms of closeness centrality instead of computing the centrality of each entity independently, and shares intermediate results between centrality computations.
Abstract: Many of today's applications can benefit from the discovery of the most central entities in real-world networks. This paper presents a new technique that efficiently finds the k most central entities in terms of closeness centrality. Instead of computing the centrality of each entity independently, our technique shares intermediate results between centrality computations. Since the cost of each centrality computation may vary substantially depending on the choice of the previous computation, our technique schedules centrality computations in a manner that minimizes the estimated completion time. This technique also updates, with negligible overhead, an upper bound on the centrality of every entity. Using this information, our technique proactively skips entities that cannot belong to the final answer. This paper presents evaluation results for actual networks to demonstrate the benefits of our technique.
43 citations
TL;DR: A method for motif-based centrality analysis is presented and two extensions are discussed which broaden the idea of motif- based centrality to specific functions of particular motif elements, and to the consideration of classes of related motifs.
43 citations
TL;DR: An improved routing strategy is proposed for enhancing the traffic capacity of scale-free networks by derived on the basis of the expanding betweenness centrality of nodes, which gives an estimate of the traffic handled by the vertex for a certain route set.
Abstract: In this paper, an improved routing strategy is proposed for enhancing the traffic capacity of scale-free networks. Instead of using the information of degree and betweenness centrality, the new algorithm is derived on the basis of the expanding betweenness centrality of nodes, which gives an estimate of the traffic handled by the vertex for a certain route set. Since the nodes with large betweenness centrality are more susceptible to traffic congestion, the traffic can be improved by redistributing traffic loads from nodes with large betweenness centrality to nodes with small betweenness centrality in the process of computing the collective routing table. Comparing with results of previous routing strategies, it is shown that the present improved routing performs more effectively.
43 citations
TL;DR: In this article, the authors applied centrality measures to reanalyze existing concept maps from a recent investigation and demonstrated that centrality is a useful measure of knowledge structure contained in these team concept map artifacts that allows researchers to infer problem representation start and goal state transitions during problem solving.
Abstract: Problem solving likely involves at least two broad stages, problem space representation and then problem solution (Newell and Simon, Human problem solving, 1972). The metric centrality that Freeman (Social Networks 1:215–239, 1978) implemented in social network analysis is offered here as a potential measure of both. This development research study applied centrality measures to reanalyze existing concept maps from a recent investigation (Engelmann and Hesse, Computer-Supported Collaborative Learning 5:299–319, 2010). Participants (N = 120) were randomly assigned to interdependent (i.e. hidden profiles) or non-interdependent conditions to work online in triads using CmapTools software to create a concept map in order to solve a problem scenario. The centrality values of these group-created concept maps agreed with the common relations count analysis used in that investigation and allowed for additional comparisons as well as analysis by multidimensional scaling. Specifically, the interdependent triad maps resembled the fully explicated problem space, while the non-interdependent triad maps mainly resembled the problem solution. The results demonstrate that centrality is a useful measure of knowledge structure contained in these team concept map artifacts that allows researchers to infer problem representation start and goal state transitions during problem solving.
42 citations
TL;DR: A number of approaches for exploiting modern network theory to help describe and analyze different data sets and problems associated with proteomic data are considered and may help scientists from a mathematics and physics background to understand where they may apply their expertise.
Abstract: The size and nature of data collected on gene and protein interactions has led to a rapid growth of interest in graph theory and modern techniques for describing, characterizing and comparing networks. Simultaneously, this is a field of growth within mathematics and theoretical physics, where the global properties, and emergent behavior of networks, as a function of the local properties has long been studied. In this review, a number of approaches for exploiting modern network theory to help describe and analyze different data sets and problems associated with proteomic data are considered. This review aims to help biologists find their way towards useful ideas and references, yet may also help scientists from a mathematics and physics background to understand where they may apply their expertise.
42 citations