scholarly journals A Weighted Statistical Network Modeling Approach to Product Competition Analysis

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Yaxin Cui ◽  
Faez Ahmed ◽  
Zhenghui Sha ◽  
Lijun Wang ◽  
Yan Fu ◽  
...  
Keyword(s):  
Network Model ◽  
Network Modeling ◽  
Network Models ◽  
Product Attributes ◽  
Weighted Network ◽  
Random Graph Model ◽  
Modeling Approach ◽  
Customer Preferences ◽  
Product Competition ◽  
Binary Network

Statistical network models have been used to study the competition among different products and how product attributes influence customer decisions. However, in existing research using network-based approaches, product competition has been viewed as binary (i.e., whether a relationship exists or not), while in reality, the competition strength may vary among products. In this paper, we model the strength of the product competition by employing a statistical network model, with an emphasis on how product attributes affect which products are considered together and which products are ultimately purchased by customers. We first demonstrate how customers’ considerations and choices can be aggregated as weighted networks. Then, we propose a weighted network modeling approach by extending the valued exponential random graph model to investigate the effects of product features and network structures on product competition relations. The approach that consists of model construction, interpretation, and validation is presented in a step-by-step procedure. Our findings suggest that the weighted network model outperforms commonly used binary network baselines in predicting product competition as well as market share. Also, traditionally when using binary network models to study product competitions and depending on the cutoff values chosen to binarize a network, the resulting estimated customer preferences can be inconsistent. Such inconsistency in interpreting customer preferences is a downside of binary network models but can be well addressed by the proposed weighted network model. Lastly, this paper is the first attempt to study customers’ purchase preferences (i.e., aggregated choice decisions) and car competition (i.e., customers’ co-consideration decisions) together using weighted directed networks.

A Weighted Network Modeling Approach for Analyzing Product Competition

2021 ◽  
Author(s):  
Yaxin Cui ◽  
Faez Ahmed ◽  
Zhenghui Sha ◽  
Lijun Wang ◽  
Yan Fu ◽  
...  
Keyword(s):  
Network Modeling ◽  
Weighted Network ◽  
Modeling Approach ◽  
Product Competition

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.

scholarly journals Predicting mobile apps spread: An epidemiological random network modeling approach

SIMULATION ◽  
2017 ◽  
Vol 94 (2) ◽  
pp. 123-130 ◽  
Author(s):  
Juan Alegre-Sanahuja ◽  
Juan-Carlos Cortés ◽  
Rafael-Jacinto Villanueva ◽  
Francisco-José Santonja
Keyword(s):  
Network Model ◽  
Mobile Applications ◽  
Mobile Apps ◽  
Random Network ◽  
Network Modeling ◽  
Numerical Results ◽  
Business Managers ◽  
Short Term ◽  
Modeling Approach ◽  
Proposed Model

The mobile applications business is a really big market, growing constantly. In app marketing, a key issue is to predict future app installations. The influence of the peers seems to be very relevant when downloading apps. Therefore, the study of the evolution of mobile apps spread may be approached using a proper network model that considers the influence of peers. Influence of peers and other social contagions have been successfully described using models of epidemiological type. Hence, in this paper we propose an epidemiological random network model with realistic parameters to predict the evolution of downloads of apps. With this model, we are able to predict the behavior of an app in the market in the short term looking at its evolution in the early days of its launch. The numerical results provided by the proposed network are compared with data from real apps. This comparison shows that predictions improve as the model is fed back. Marketing researchers and strategy business managers can benefit from the proposed model since it can be helpful to predict app behavior over the time anticipating the spread of an app.

scholarly journals Network models for mapping educational data

2017 ◽  
Vol 3 ◽  
Author(s):  
Karen E. Willcox ◽  
Luwen Huang
Keyword(s):  
Adaptive Learning ◽  
Learning Analytics ◽  
Gap Analysis ◽  
Network Modeling ◽  
Network Models ◽  
Curriculum Mapping ◽  
Modeling Approach ◽  
Scalable Learning ◽  
Learning Pathways ◽  
Curricular Elements

Educational mapping is the process of analyzing an educational system to identify entities, relationships and attributes. This paper proposes a network modeling approach to educational mapping. Current mapping processes in education typically represent data in forms that do not support scalable learning analytics. For example, a curriculum map is usually a table, where relationships among curricular elements are represented implicitly in the rows of the table. The proposed network modeling approach overcomes this limitation through explicit modeling of these relationships in a graph structure, which in turn unlocks the ability to perform scalable analyses on the dataset. The paper presents network models for educational use cases, with concrete examples in curriculum mapping, accreditation mapping and concept mapping. Illustrative examples demonstrate how the formal modeling approach enables visualization and learning analytics. The analysis provides insight into learning pathways, supporting design of adaptive learning systems. It also permits gap analysis of curriculum coverage, supporting student advising, student degree planning and curricular design at scales ranging from an entire institution to an individual course.

Module Similarity Measure Method of Products Based on Assembly Relationship

2014 ◽  
Vol 607 ◽  
pp. 721-726
Author(s):  
Yan Hui Chen ◽  
De Jian Zhou ◽  
Zhao Hua Wu
Keyword(s):  
Network Model ◽  
Similarity Measure ◽  
Network Modeling ◽  
Modeling Method ◽  
Weighted Network ◽  
Coding Method ◽  
Node Similarity ◽  
Network Similarity

A module similarity measure method of the mechanical and electrical products is studied in this paper. First this paper proposes the module assembly type coding method and network modeling method, and establishes its weighted network model by taking typical module as an example; then it studies the assembly relationship similarity, node similarity and network similarity; finally it provides the module similarity measure method based on the assembly relationship.

Study of Phase Distribution of a Liquid-Solid Circulating Fluidized Bed Reactor Using Abductive Network Modeling Approach

2013 ◽  
Vol 8 (2) ◽  
pp. 77-91 ◽  
Author(s):  
Shaikh A. Razzak
Keyword(s):  
Fluidized Bed ◽  
Network Model ◽  
Circulating Fluidized Bed ◽  
Phase Distribution ◽  
Network Modeling ◽  
Absolute Error ◽  
Pilot Scale ◽  
Cross Sectional ◽  
Modeling Approach ◽  
Abductive Network

Abstract This communication deals with the Abductive Network modeling approach to investigate the phase holdup distributions of a liquid–solid circulating fluidized bed (LSCFB) system. The Abductive Network model is developed/trained using experimental data collected from a pilot scale LSCFB reactor involving 500-μm size glass beads and water as solid and liquid phases, respectively. The trained Abductive Network model successfully predicted experimental phase holdups of the LSCFB riser under different operating parameters. It is observed that the model predicted cross-sectional average of solids holdups in the axial directions and radial flow structure are well agreement with the experimental values. The statistical performance indicators including the mean absolute error (~4.67%) and the correlation coefficient (0.992) also show favorable indications of the suitability of Abductive Network modeling approach in predicting the solids holdup of the LSCFB system.

The configuration model

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Degree Sequence ◽  
Graph Model ◽  
Network Models ◽  
Clustering Coefficient ◽  
Giant Component ◽  
Configuration Model ◽  
Bipartite Networks ◽  
Small Component ◽  
Random Graph Model ◽  
Average Size

A discussion of the most fundamental of network models, the configuration model, which is a random graph model of a network with a specified degree sequence. Following a definition of the model a number of basic properties are derived, including the probability of an edge, the expected number of multiedges, the excess degree distribution, the friendship paradox, and the clustering coefficient. This is followed by derivations of some more advanced properties including the condition for the existence of a giant component, the size of the giant component, the average size of a small component, and the expected diameter. Generating function methods for network models are also introduced and used to perform some more advanced calculations, such as the calculation of the distribution of the number of second neighbors of a node and the complete distribution of sizes of small components. The chapter ends with a brief discussion of extensions of the configuration model to directed networks, bipartite networks, networks with degree correlations, networks with high clustering, and networks with community structure, among other possibilities.

scholarly journals Likelihood-based approach to discriminate mixtures of network models that vary in time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Naomi A. Arnold ◽  
Raul J. Mondragón ◽  
Richard G. Clegg
Keyword(s):  
Network Model ◽  
Change Point ◽  
Network Science ◽  
Preferential Attachment ◽  
Network Models ◽  
Explanatory Models ◽  
Growth Mechanisms ◽  
Artificial Data ◽  
Network Growth ◽  
Over Time

AbstractDiscriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure, with a wealth of explanatory models based on these. These models are deliberately simple, commonly with the network growing according to a constant mechanism for its lifetime, to allow for analytical results. We use a likelihood-based framework on artificial data where the network model changes at a known point in time and demonstrate that we can recover the change point from analysis of the network. We then use real datasets and demonstrate how our framework can show the changing importance of network growth mechanisms over time.

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.

Can Network Models Represent Personality Structure and Processes Better than Trait Models Do?

2012 ◽  
Vol 26 (4) ◽  
pp. 444-445 ◽  
Author(s):  
Tobias Rothmund ◽  
Anna Baumert ◽  
Manfred Schmitt
Keyword(s):  
Network Model ◽  
Explanatory Power ◽  
Network Models ◽  
Personality Structure ◽  
Psychological Processes ◽  
Target Article ◽  
Flexible Framework ◽  
Trait Model ◽  
Better Than

We argue that replacing the trait model with the network model proposed in the target article would be immature for three reasons. (i) If properly specified and grounded in substantive theories, the classic state–trait model provides a flexible framework for the description and explanation of person × situation transactions. (ii) Without additional substantive theories, the network model cannot guide the identification of personality components. (iii) Without assumptions about psychological processes that account for causal links among personality components, the concept of equilibrium has merely descriptive value and lacks explanatory power. Copyright © 2012 John Wiley & Sons, Ltd.

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