scholarly journals Co-citations in context: Disciplinary heterogeneity is relevant

2020 ◽  
Vol 1 (1) ◽  
pp. 264-276 ◽  
Author(s):  
James Bradley ◽  
Sitaram Devarakonda ◽  
Avon Davey ◽  
Dmitriy Korobskiy ◽  
Siyu Liu ◽  
...  
Keyword(s):  
Citation Analysis ◽  
Scientific Literature ◽  
Frame Of Reference ◽  
Citation Networks ◽  
Graph Models ◽  
Disciplinary Boundaries ◽  
Random Graph Models ◽  
Organization Of Science ◽  
Insight Into ◽  
Highly Cited

Citation analysis of the scientific literature has been used to study and define disciplinary boundaries, to trace the dissemination of knowledge, and to estimate impact. Co-citation, the frequency with which pairs of publications are cited, provides insight into how documents relate to each other and across fields. Co-citation analysis has been used to characterize combinations of prior work as conventional or innovative and to derive features of highly cited publications. Given the organization of science into disciplines, a key question is the sensitivity of such analyses to frame of reference. Our study examines this question using semantically themed citation networks. We observe that trends reported to be true across the scientific literature do not hold for focused citation networks, and we conclude that inferring novelty using co-citation analysis and random graph models benefits from disciplinary context.

Networks

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Biological Networks ◽  
Generative Models ◽  
The Internet ◽  
Network Data ◽  
Computer Algorithms ◽  
Graph Models ◽  
Random Graph Models ◽  
The Social ◽  
Spectral Algorithms ◽  
Wide Availability

The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in recent years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyse network data on an unprecendented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social science. This book brings together the most important breakthroughts in each of these fields and presents them in a unified fashion, highlighting the strong interconnections between work in different areas. Topics covered include the measurement of networks; methods for analysing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms, including spectral algorithms and community detection; mathematical models of networks such as random graph models and generative models; and models of processes taking place on networks.

scholarly journals Exponential random graph models for little networks

2021 ◽  
Vol 64 ◽  
pp. 225-238
Author(s):  
George G. Vega Yon ◽  
Andrew Slaughter ◽  
Kayla de la Haye
Keyword(s):  
Random Graph ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph

Why Legislative Networks? Analyzing Legislative Network Formation

2017 ◽  
Vol 7 (3) ◽  
pp. 505-522 ◽  
Author(s):  
Stefan Wojcik
Keyword(s):  
Political Parties ◽  
Network Formation ◽  
Contemporary Literature ◽  
Party Organization ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Personal Traits ◽  
Random Graph Models ◽  
Exponential Random Graph ◽  
The Social

Are the social networks of legislators affected more by their political parties or their personal traits? How does the party organization influence the tendency of members to work collectively on a day-to-day basis? In this paper, I explore the determinants of the relationships of legislators in the Brazilian Chamber of Deputies. I use exponential random graph models to evaluate the relative influence of personal traits versus party influence in generating legislator relationships. Despite a focus on personalism in Brazil, the analysis reveals that the effects of political parties on tie formation are roughly equal to the effects of personal traits, suggesting that networks may make political parties much more cohesive than contemporary literature would lead us to believe.

scholarly journals Propiedades psicométricas de la Escala de Interacción en las Clases Dirigidas (EICD)

2016 ◽  
Vol 12 (3) ◽  
pp. 840 ◽  
Author(s):  
Adrián Pastor-Barceló ◽  
Vicente Prado-Gascó ◽  
Pilar Bustillo-Casero
Keyword(s):  
Design Methodology ◽  
Scientific Literature ◽  
Small Sample ◽  
Improve Quality ◽  
Validity And Reliability ◽  
Final Version ◽  
First Time ◽  
To Receive ◽  
Insight Into

Purpose: This research focuses on the construction and validation of a scale designed to assess the quality of the supervised classes: Interaction on Supervised Classes Scale (ISCS).Design/methodology/approach: This is a descriptive correlational study. For the construction of the scale three phases were performed in which different experts assessed the adequacy of the items. Finally, the psychometric properties of the final version were studied in a sample of 314 consumers (69.1% women) aged between 18 and 77 with an average of 39.33 years (SD=12.25).Findings: The scale presents adequate validity and reliability, being a useful tool for measuring the interaction in Supervised Classes.Research limitations/implications: The sampling, non-probabilistic or convenience, have taken the sample of a unique sports facility and the small sample size.Practical implications: The ISCS allows managers to receive better feedback, allowing them to obtain deeper insight into the quality and satisfaction of the service. According to its results, the managers may implement different strategies to improve quality in a key service within sports centers.Originality/value: For the first time the interaction between customers and between customers and employees is evaluated both inside and outside the center, a topic that had not yet been studied in the scientific literature. The scale can be applied to any type of directed activity, and will allow a greater understanding of the quality of service.

Modularity in several random graph models

2017 ◽  
Vol 61 ◽  
pp. 947-953 ◽  
Author(s):  
Liudmila Ostroumova Prokhorenkova ◽  
Paweł Prałat ◽  
Andrei Raigorodskii
Keyword(s):  
Random Graph ◽  
Graph Models ◽  
Random Graph Models

scholarly journals Exponential-family Random Graph Models for Rank-order Relational Data

2017 ◽  
Vol 47 (1) ◽  
pp. 68-112 ◽  
Author(s):  
Pavel N. Krivitsky ◽  
Carter T. Butts
Keyword(s):  
General Framework ◽  
Exponential Family ◽  
Rank Order ◽  
Relative Volume ◽  
Relational Data ◽  
Interpersonal Interaction ◽  
Graph Models ◽  
Cross Sectional ◽  
Sufficient Statistics ◽  
Random Graph Models

Rank-order relational data, in which each actor ranks other actors according to some criterion, often arise from sociometric measurements of judgment or preference. The authors propose a general framework for representing such data, define a class of exponential-family models for rank-order relational structure, and derive sufficient statistics for interdependent ordinal judgments that do not require the assumption of comparability across raters. These statistics allow estimation of effects for a variety of plausible mechanisms governing rank structure, both in a cross-sectional context and evolving over time. The authors apply this framework to model the evolution of liking judgments in an acquaintance process and to model recall of relative volume of interpersonal interaction among members of a technology education program.

scholarly journals Random graph models for directed acyclic networks

2009 ◽  
Vol 80 (4) ◽  
Author(s):  
Brian Karrer ◽  
M. E. J. Newman
Keyword(s):  
Random Graph ◽  
Graph Models ◽  
Random Graph Models
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