Network theory

About: Network theory is a research topic. Over the lifetime, 2257 publications have been published within this topic receiving 109864 citations.

Noise Properties of Four-Pole Networks with Application to Parametric Devices

Abstract: The ability to employ a new concept profitably is a matter of representation. If it can be shown to be of a kind with older and more familiar building blocks, and if a common formalism may be demonstrated, then the process of understanding is a good deal simpler. The parametric amplifier is, for all practical purposes, a product of recent technology, although its history can be traced for at least 100 years. Its mechanisms seem different from conventional structures, although it bears a dubious resemblance to mixers. Further, the noise processes come from signal and image sources, and some sort of new bookkeeping seems necessary for accounting purposes. It is the purpose of this paper to integrate the parametric amplifier into more conventional network theory. It is a linear network amply covered by general theorems applicable to all linear networks. However, because the noise theory of the parametric device is so relevant, these theorems will be expanded to permit general noise analysis.

Analysis of brain network dynamics estimated from fMRI data: A new framework based on communicability and flow

Abstract: Neuroimaging techniques such as MRI have been widely used to explore the associations between brain areas Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication However, the application of network theory to study FC matrices is often "static" despite the dynamic nature of time series obtained from fMRI The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics Technically, we tune a multivariate Ornstein-Uhlenbeck process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for EC as well as input properties (similar to local excitabilities) The network analysis is then based on the network response (or Green function) that describes the interactions between nodes across time for the estimated dynamics This model-based approach provides time-dependent graph-like descriptors -communicability and flow- that characterize the roles that either nodes or connections play in the propagation of activity within the network They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (eg, random network or ring lattice) In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account Our results show a merging of functional communities over time (in which input properties play a role), moving from segregated to global integration of the network activity Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity

Inter-regional betweenness centrality in the European R&D network: Empirical investigation using European Framework data

Abstract: An overarching concern in regional science is the characterization of interactions—such as commuter flows, transport, migration, or knowledge flows—within and between subnational spatial units. In this work, we use techniques from social network analysis to address the quality, rather than the quantity, of such interactions. Given the great current interest in European RD the fraction of the shortest paths on which an edge occurs is defined as the edge betweenness centrality. Edges with high betweenness centrality have the greatest load, are strategically positioned, and potentially can act as bottlenecks for the flows. We use this idea to evaluate knowledge flows between organizations in the European R&D network, considering several ways to relate the betweenness centrality at the level of FP project participants to knowledge flows at the NUTS2 regional level. We do so by aggregating betweenness centrality values calculated using bipartite graphs linking organizations to the FP projects in which they participate, considering annual FP data between the years 1999 and 2006. We determine the most central inter-regional knowledge flows, describe how this changes over time, and consider the implications for knowledge flows in European R&D networks. We model the centrality of the flows by means of spatial interaction models, estimating how geographical, technological, and social factors influence which region pairs become bottlenecks in the flow of knowledge. The results have meaningful implications to European R&D policy, in particular concerning which region pairs constitute the core in European R&D networks and which mechanisms drive the formation of this regional core. Keywords: European R&D networks, social network analysis, betweenness centrality, Framework Programmes JEL codes: L14, O31, R12

A New Network Robustness Topology Measure based on Information Theory

Abstract: Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA(Dated: November 3, 2014)A crucial challenge in network theory is to study how robust a network is when facing failuresor attacks. In this work, we propose a novel methodology to measure the topological resilience androbustness of a network based on Information Theory quanti ers. This measure can be used withany probability distribution able to represent the network’s properties. In particular, we analyzethe eciency in capturing small perturbations in the network’s topology when using the degree anddistance distributions. Theoretical examples and real networks are used to study the performanceof this methodology. Although both cases show to be able to detect any single topological change,the distance distribution seems to be more consistent to reect the network structural deviations. Inall cases, the novel resilience and robustness measures computed by using the distance distributionreect better the consequences of the failures, outperforming other methods.

Evaluating the Project Coordination Performance Using Information Centrality

Abstract: Evaluation of coordination performance in a project network requires reliable measures and monitoring methods for effective management. Recent literature includes studies addressing the relationship between coordinative activity and the configuration of communication networks. In these works, the role of network centrality is investigated through the basic standard centrality measures of degree, betweenness and closeness. Current social network analysis research emphasizes new formulations of centrality measures for robust structural analysis of project networks. This paper presents a novel approach for measuring network centrality using the concept of information centrality. It is based on the idea that all paths carry information. The significance of information centrality values for the actors in a wayfinding signage project at Istanbul Sabiha Gokcen International Airport is investigated. A centrality index is defined for each firm based on the average of the four measures of centrality. Findings suggest the existence of a high correlation between coordination scores and the centrality indices. A centrality index augmented by information centrality measure has potentials for assessing the coordination performance in construction management research, and it is promising for the structural analysis of project communication networks.