Network Modeling

2010 ◽  
pp. 113-121 ◽  
Author(s):  
Kevin M. Curtin
Keyword(s):  
Graph Theory ◽  
Theoretical Basis ◽  
Network Modeling ◽  
Network Models ◽  
Data Models ◽  
Flow Modeling ◽  
Important Data ◽  
Network Analyses ◽  
Topological Relationships ◽  
Mathematical Discipline

Network models are some of the earliest and most consistently important data models in GISystems. Network modeling has a strong theoretical basis in the mathematical discipline of graph theory, and methods for describing and measuring networks and proving properties of networks are well-developed. There are a variety of network models in GISystems, which are primarily differentiated by the topological relationships they maintain. Network models can act as the basis for location through the process of linear referencing. Network analyses such as routing and flow modeling have to some extent been implemented, although there are substantial opportunities for additional theoretical advances and diversified application.

scholarly journals Investigating the Structure of Intelligence Using Latent Variable and Psychometric Network Modeling: A Commentary and Reanalysis

2021 ◽  
Vol 9 (1) ◽  
pp. 8
Author(s):  
Christopher J. Schmank ◽  
Sara Anne Goring ◽  
Kristof Kovacs ◽  
Andrew R. A. Conway
Keyword(s):  
Cognitive Processes ◽  
Latent Variable ◽  
Network Modeling ◽  
Network Models ◽  
Mental Ability ◽  
Variable Model ◽  
General Mental Ability ◽  
Theories Of Intelligence ◽  
Psychological Theories ◽  
Model Preference

In a recent publication in the Journal of Intelligence, Dennis McFarland mischaracterized previous research using latent variable and psychometric network modeling to investigate the structure of intelligence. Misconceptions presented by McFarland are identified and discussed. We reiterate and clarify the goal of our previous research on network models, which is to improve compatibility between psychological theories and statistical models of intelligence. WAIS-IV data provided by McFarland were reanalyzed using latent variable and psychometric network modeling. The results are consistent with our previous study and show that a latent variable model and a network model both provide an adequate fit to the WAIS-IV. We therefore argue that model preference should be determined by theory compatibility. Theories of intelligence that posit a general mental ability (general intelligence) are compatible with latent variable models. More recent approaches, such as mutualism and process overlap theory, reject the notion of general mental ability and are therefore more compatible with network models, which depict the structure of intelligence as an interconnected network of cognitive processes sampled by a battery of tests. We emphasize the importance of compatibility between theories and models in scientific research on intelligence.

Bayesian Network Modeling of Transcription Factor Binding Sites

2007 ◽  
pp. 300-318
Author(s):  
Vipin Narang ◽  
Rajesh Chowdhary ◽  
Ankush Mittal ◽  
Wing-Kin Sung
Keyword(s):  
Transcription Factor ◽  
Parameter Estimation ◽  
Bayesian Network ◽  
Network Modeling ◽  
Network Models ◽  
Transcription Factor Binding Sites ◽  
Physical Problem ◽  
Network Applications ◽  
Bayesian Network Modeling ◽  
Bayesian Network Models

A predicament that engineers who wish to employ Bayesian networks to solve practical problems often face is the depth of study required in order to obtain a workable understanding of this tool. This chapter is intended as a tutorial material to assist the reader in efficiently understanding the fundamental concepts involved in Bayesian network applications. It presents a complete step by step solution of a bioinformatics problem using Bayesian network models, with detailed illustration of modeling, parameter estimation, and inference mechanisms. Considerations in determining an appropriate Bayesian network model representation of a physical problem are also discussed.

scholarly journals Group Measures and Modeling for Social Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Vincent Levorato
Keyword(s):  
Complex System ◽  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Modeling ◽  
Complex Interactions ◽  
Social Network Modeling

Social network modeling is generally based on graph theory, which allows for study of dynamics and emerging phenomena. However, in terms of neighborhood, the graphs are not necessarily adapted to represent complex interactions, and the neighborhood of a group of vertices can be inferred from the neighborhoods of each vertex composing that group. In our study, we consider that a group has to be considered as a complex system where emerging phenomena can appear. In this paper, a formalism is proposed to resolve this problematic by modeling groups in social networks using pretopology as a generalization of the graph theory. After giving some definitions and examples of modeling, we show how some measures used in social network analysis (degree, betweenness, and closeness) can be also generalized to consider a group as a whole entity.

A framework to learn graph theory using simple wireless network models

2016 ◽  
Vol 24 (6) ◽  
pp. 843-852 ◽  
Author(s):  
Adriana Dapena ◽  
Francisco J. Vázquez-Araujo ◽  
Paula M. Castro ◽  
Maria J. Souto-Salorio
Keyword(s):  
Graph Theory ◽  
Wireless Network ◽  
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.

COUPLING MULTIPHASE PORE-SCALE MODELS TO ACCOUNT FOR BOUNDARY CONDITIONS: APPLICATION TO 2D QUASI-STATIC PORE NETWORKS

2011 ◽  
Vol 03 (03) ◽  
pp. 109-131 ◽  
Author(s):  
ROBERT THOMAS PETERSEN ◽  
MATTHEW THOMAS BALHOFF ◽  
STEVEN BRYANT
Keyword(s):  
Boundary Conditions ◽  
Flow Behavior ◽  
Network Modeling ◽  
Network Models ◽  
Coupling Scheme ◽  
Pore Scale ◽  
Nuclear Waste Storage ◽  
Flow And Transport ◽  
Transport Behavior ◽  
Macroscopic Properties

Accurate predictions of macroscopic multiphase flow properties (relative permeability and capillary pressure) are necessary for modeling flow and transport in subsurface applications, such as hydrocarbon recovery, carbon sequestration and nuclear waste storage. These properties are usually measured experimentally, but pore-scale network modeling has become an efficient alternative for understanding fundamental flow behavior and predicting macroscopic properties. In many cases, network modeling gives excellent agreement with experiment by using models physically representative of real media. Void space within a rock sample can be extracted from high resolution images and converted to a topologically equivalent network of pores and throats. Multiphase fluid transport is then modeled in the network and macroscopic properties extracted from the model. Advancements continue to be made in making multiphase network models (both quasistatic and dynamic) predictive, but one limitation is that arbitrary (e.g., constant pressure) boundary conditions are usually assumed; they do not reflect the local saturations and pressure distributions that are affected by flow and transport in the surrounding media. In this work we demonstrate that transport behavior at the pore scale, and therefore, upscaled macroscopic properties are directly affected by the boundary conditions. Pore-scale drainage in 2D quasi-static networks is modeled by direct coupling to other pore-network models so that the boundary conditions reflect local variations of transport behavior in the surrounding media. Phase saturations are coupled at model boundaries to ensure continuity between adjacent models. Macroscopic petrophysical properties are shown to be largely dependent upon the surrounding media, which are manifested in the form of boundary conditions. The predictive ability of network simulations is thus improved using the novel network coupling scheme.

scholarly journals Psychometric Models of Individual Differences in Reading Comprehension: A Reanalysis of Freed, Hamilton, and Long (2017)

2019 ◽  
Author(s):  
Sara Anne Goring ◽  
Christopher J. Schmank ◽  
Michael J. Kane ◽  
Andrew R. A. Conway
Keyword(s):  
Reading Comprehension ◽  
Individual Differences ◽  
Cognitive Abilities ◽  
Latent Variable ◽  
Convergent Validity ◽  
Cognitive Task ◽  
Network Modeling ◽  
Network Models ◽  
Measurement Model ◽  
Common Cause

Individual differences in reading comprehension have often been explored using latent variable modeling (LVM), to assess the relative contribution of domain-general and domain-specific cognitive abilities. However, LVM is based on the assumption that the observed covariance among indicators of a construct is due to a common cause (i.e., a latent variable; Pearl, 2000). This is a questionable assumption when the indicator variables are measures of performance on complex cognitive tasks. According to Process Overlap Theory (POT; Kovacs & Conway, 2016), multiple processes are involved in cognitive task performance and the covariance among tasks is due to the overlap of processes across tasks. Instead of a single latent common cause, there are thought to be multiple dynamic manifest causes, consistent with an emerging view in psychometrics called network theory (Barabási, 2012; Borsboom & Cramer, 2013). In the current study, we reanalyzed data from Freed et al. (2017) and compared two modeling approaches: LVM (Study 1) and psychometric network modeling (Study 2). In Study 1, two exploratory LVMs demonstrated problems with the original measurement model proposed by Freed et al. Specifically, the model failed to achieve discriminant and convergent validity with respect to reading comprehension, language experience, and reasoning. In Study 2, two network models confirmed the problems found in Study 1, and also served as an example of how network modeling techniques can be used to study individual differences. In conclusion, more research, and a more informed approach to psychometric modeling, is needed to better understand individual differences in reading comprehension.

Behavioral and Network Modeling for Efficient Design of Adaptive Systems

2014 ◽  
Author(s):  
Peter Schneider ◽  
Andreas Köhler ◽  
Sven Reitz ◽  
Roland Jancke
Keyword(s):  
Adaptive Systems ◽  
Network Modeling ◽  
Algorithm Design ◽  
Network Models ◽  
Adaptive Signal Processing ◽  
System Level ◽  
Mathematical Representation ◽  
Efficient Design ◽  
And Control ◽  
Adaptive Signal

Adaptive systems usually implement the entire cycle of measurement and data acquisition, signal conditioning and processing as well as process control. Especially, for the design of adaptive signal processing and control algorithms detailed insight into the interaction between the system components is of crucial importance. System level simulations are a suitable way to gain insight and to support algorithm design and test. However, an adequate mathematical representation of the system behavior is needed to take advantage of this method. In the paper a generic methodology for behavioral modelling is introduced. Important steps of the modelling process are described and illustrated by two examples. For a gyro sensor system the combination of different modeling methods is demonstrated. Network modeling and in particular an approach for the construction of network models for magnetic systems is discussed for an electromagnetic switching device.

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