Network models of concentrated polymer solutions derived from the yamamoto network theory

1981 ◽  
Vol 19 (4) ◽  
pp. 531-555 ◽  
Author(s):  
G. G. Fuller ◽  
L. G. Leal
Keyword(s):  
Network Theory ◽  
Polymer Solutions ◽  
Network Models

A Weighted Network Modeling Approach for Analyzing Product Competition

2020 ◽  
Author(s):  
Yaxin Cui ◽  
Faez Ahmed ◽  
Zhenghui Sha ◽  
Lijun Wang ◽  
Yan Fu ◽  
...  
Keyword(s):  
Network Theory ◽  
Network Modeling ◽  
Network Models ◽  
Social Aspects ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph ◽  
The Social ◽  
Product Competition

Abstract Statistical network models allow us to study the co-evolution between the products and the social aspects of a market system, by modeling these components and their interactions as graphs. In this paper, we study competition between different car models using network theory, with a focus on how product attributes (like fuel economy and price) affect which cars are considered together and which cars are finally bought by customers. Unlike past work, where most systems have been studied with the assumption that relationships between competitors are binary (i.e., whether a relationship exists or not), we allow relationships to take strengths (i.e., how strong a relationship is). Specifically, we use valued Exponential Random Graph Models and show that our approach provides a significant improvement over the baselines in predicting product co-considerations as well as in the validation of market share. This is also the first attempt to study aggregated purchase preference and car competition using valued directed networks.

Nonaffine Transient Network Theory of Associating Polymer Solutions

2006 ◽  
Vol 39 (17) ◽  
pp. 5913-5920 ◽  
Author(s):  
Fumihiko Tanaka ◽  
Tsuyoshi Koga
Keyword(s):  
Network Theory ◽  
Polymer Solutions ◽  
Transient Network ◽  
Associating Polymer

Sensitivity Analysis of the Networks with Fractional Order Reactance

2013 ◽  
Vol 860-863 ◽  
pp. 2309-2314
Author(s):  
Gui Shu Liang ◽  
Xing Hua Zheng ◽  
Long Ma ◽  
Hua Ying Dong
Keyword(s):  
Sensitivity Analysis ◽  
Fractional Calculus ◽  
Fractional Order ◽  
Network Theory ◽  
Network Models ◽  
Element Model ◽  
Network Sensitivity

Fractional calculus theory has gained more and more applications in numerous fields. In many cases, using fractional reactance element model can describe the properties of objects more accurately and simply. This paper studies the sensitivity of networks with fractional order reactance, puts forward the adjoint network sensitivity formulas and the incremental network models of fractional order capacitor and inductor, which will further develop the adjoint network and Incremental network theory. The simulation verification is also given.

scholarly journals Building-network: Concept, generation method and centrality analysis

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Xiao Wang ◽  
Dirk Burghardt
Keyword(s):  
Network Theory ◽  
Network Models ◽  
Pattern Detection ◽  
Centrality Measures ◽  
Concept Generation ◽  
Eigenvector Centrality ◽  
Rural Regions ◽  
Network Concept ◽  
Centrality Analysis ◽  
Refinement Strategy

<p><strong>Abstract.</strong> Buildings are among the most important features of cities. In the suburban or rural regions, buildings are normally constructed along the roads, which forms the smooth and consistent patterns so that the building arrangements also can be described with network models. In previous studies, network theory has achieved good performance in cartography and GIS. In this paper, a study of a building-network is proposed, including the concepts, generation methods and centrality analysis. Firstly, with the constraint Delaunay triangulation and the refinement strategy by facing ratio, the building-network is generated by considering the buildings and the proximal segments as the nodes and segments of the network, respectively. Then, centrality analysis is applied on the building-network, aiming to reveal the crucial relationships among buildings, which is useful for understanding the structural properties of the complex network. Four different centrality measures, i.e. degree, closeness, betweenness, and eigenvector centrality, are calculated based on the building-networks. The buildings show different distribution effects and patterns under the four centrality measures. From the results, the degree centrality reveals the local centre of the region; closeness and eigenvector centrality have the ability to cluster buildings into different groups; while betweenness centrality can detect the linear patterns. Therefore, using network theory to analyse buildings can reveal some inner relationships of buildings and has great potential in the application of building pattern detection, classification, clustering and further generalization.</p>

An Analysis of Modularity As a Design Rule Using Network Theory

2018 ◽  
Author(s):  
Hannah S. Walsh ◽  
Andy Dong ◽  
Irem Y. Tumer
Keyword(s):  
Network Theory ◽  
Design Principle ◽  
Network Models ◽  
Design Rule ◽  
Function Parameter ◽  
Engineering Systems ◽  
System Architectures ◽  
System Representation ◽  
And Function ◽  
Random Failures

Increasing the modularity of system architectures is generally accepted as a good design principle in engineering. In this paper, we explore whether modularity comes at the expense of robustness. To that end, we model three engineering systems as networks and measure the relation between modularity and robustness to random failures. We produced four types of network models of systems — component, component-function, component-parameter, and function-parameter — to further test the relation of robustness to the type of system representation, architectural or behavioral. The results show that higher modularity is correlated with lower robustness (p < 0.001) and that the estimated modularity of the system can depend on the type of system representation. The implication is that there is a trade-off between modularity and robustness, meaning that increasing modularity might not be appropriate for systems for which robustness is critical and for those whose modularity estimate differs largely between each type of system representation.

An Analysis of Modularity as a Design Rule Using Network Theory

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
Hannah S. Walsh ◽  
Andy Dong ◽  
Irem Y. Tumer
Keyword(s):  
Network Theory ◽  
Design Principle ◽  
Network Models ◽  
Design Rule ◽  
Function Parameter ◽  
Engineering Systems ◽  
System Architectures ◽  
System Representation ◽  
And Function ◽  
Random Failures

Increasing the modularity of system architectures is generally accepted as a good design principle in engineering. In this paper, we explore whether modularity comes at the expense of robustness. To that end, we model three engineering systems as networks and measure the relation between modularity and robustness to random failures. We produced four types of network models of systems—component-component, component-function, component-parameter, and function-parameter—to further test the relation of robustness to the type of system representation, architectural or behavioral. The results show that higher modularity is correlated with lower robustness (p < 0.001) and that the estimated modularity of the system can depend on the type of system representation. The implication is that there is a tradeoff between modularity and robustness, meaning that increasing modularity might not be appropriate for systems for which robustness is critical and modularity estimates differ significantly between the types of system representation.

scholarly journals Vagueness, Identity, and the Dangers of a General Metaphysics in Archaeology

2021 ◽  
Vol 4 (1) ◽  
pp. 20-35
Author(s):  
Artur Ribeiro
Keyword(s):  
Big Data ◽  
Network Theory ◽  
Bayesian Models ◽  
Network Models ◽  
Actor Network Theory ◽  
Archaeological Theory ◽  
Actor Network ◽  
Quantitative Models ◽  
Archaeological Science

AbstractArchaeology is currently bound to a series of metaphysical principles, one of which claims that reality is composed of a series of discrete objects. These discrete objects are fundamental metaphysical entities in archaeological science and posthumanist/New Materialist approaches and can be posited, assembled, counted, and consequently included in quantitative models (e.g. Big Data, Bayesian models) or network models (e.g. Actor-Network Theory). The work by Sørensen and Marila shows that archaeological reality is not that discrete, that some objects cannot be easily identified, and that perhaps reality is not always necessarily composed of discrete objects. The aim of this article is to take Sørensen and Marila’s arguments to their ultimate logical consequences: most archaeological theory today operates underthe illusion of a general metaphysics. This illusion dictates not only that all of reality is composed of discrete objects, but that since reality manifests in a certain way, there has to be a methodology that accurately represents that reality. A brief discussion on the notion of “conjecture,” as conceived in certain historical theories, is also presented.

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