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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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TL;DR: The application of network analysis to social systems involving non-human organisms has been slower, because it has been difficult to infer the statistical and biological significance of observed network statistics and structures.
Abstract: Over the past decade network theory has been applied successfully to the study of a variety of complex adaptive systems. However, the application of these techniques to non-human social networks has several shortfalls. Firstly, in most cases the strength of associations between individuals is disregarded. Secondly, present techniques assume that observed interactions are invariant values and not statistical samples taken from a population. These two simplifications have weakened the value of these techniques when applied to the study of animal social systems. Here we introduce a set of behaviorally meaningful weighted network statistics that can be readily applied to matrices of association indices between pairs of individual animals. We also introduce bootstrapping techniques that estimate the effects of sampling uncertainty on the network statistics and structure. Finally, we discuss the use of randomisation tests to detect the departure of observed network statistics from expected values under null hypotheses of random association given the sampling structure of the data. We use two case studies to show that these techniques provide invaluable insight in the dynamics of interactions within social units and in the community structure of societies.

8 citations

Journal ArticleDOI
TL;DR: In this paper, the authors discuss the bottleneck-oriented approach commonly applied today in the context of network effects in manufacturing systems and compare its performance to centrality measures from complex network theory.

8 citations

Journal ArticleDOI
TL;DR: In this article, a simple integrated framework is provided for understanding why firms collaborate and under which conditions they establish durable networks that succeed in achieving goals, which is especially relevant to policy planners and those having a perspective that goes beyond the performance of individual organizations.
Abstract: This article contributes to the general understanding of governance in networks and the achievement of private and common goals. Integrating transaction costs and social network theory, a simple integrated framework is provided for understanding why firms collaborate and under which conditions they establish durable networks that succeed in achieving goals. Network theory is extended by explicitly distinguishing between firm and network level governance, and by identifying governance mechanisms that adapt, coordinate, and safeguard customized exchanges. This way issues as how networks evolve, how they are governed, and ultimately, how collective outcomes might be generated can be better comprehended. This is especially relevant to policy planners and those having a perspective that goes beyond the performance of individual organizations.

8 citations

Journal ArticleDOI
TL;DR: This paper uses network global efficiency by removing edges to propose a new centrality measure for identifying influential nodes in complex networks, and shows that the proposed measure is more effective than the other three centrality measures.
Abstract: Identifying influential nodes is a basic measure of characterizing the structure and dynamics in complex networks. In this paper, we use network global efficiency by removing edges to propose a new centrality measure for identifying influential nodes in complex networks. Differing from the traditional network global efficiency, the proposed measure is determined by removing edges from networks, not removing nodes. Instead of static structure properties which are exhibited by other traditional centrality measures, such as degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC), we focus on the perspective of dynamical process and global structure in complex networks. Susceptible-infected (SI) model is utilized to evaluate the performance of the proposed method. Experimental results show that the proposed measure is more effective than the other three centrality measures.

8 citations

Journal ArticleDOI
TL;DR: In this article, a graph dynamic mode decomposition (GDM) is applied for collective motion classification of biological complex networks, where the graph is decomposed into a graph and the spectral properties of the graph are analyzed.
Abstract: Understanding biological network dynamics is a fundamental issue in various scientific and engineering fields. Network theory is capable of revealing the relationship between elements and their propagation; however, for complex collective motions, the network properties often transiently and complexly change. A fundamental question addressed here pertains to the classification of collective motion network based on physically-interpretable dynamical properties. Here we apply a data-driven spectral analysis called graph dynamic mode decomposition, which obtains the dynamical properties for collective motion classification. Using a ballgame as an example, we classified the strategic collective motions in different global behaviours and discovered that, in addition to the physical properties, the contextual node information was critical for classification. Furthermore, we discovered the label-specific stronger spectra in the relationship among the nearest agents, providing physical and semantic interpretations. Our approach contributes to the understanding of principles of biological complex network dynamics from the perspective of nonlinear dynamical systems.

8 citations


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