Modelling citation networks

Goldberg, Sophia; Anthony, Hannah; Evans, T. S.

doi:10.1007/s11192-015-1737-9

Cited by 44 publications

(34 citation statements)

References 49 publications

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“…Several authors have noted this increasing in various fields of knowledge (e.g., Bonilla et al, 2015;Goldberg et al, 2015;Simsek et al, 2013), pointing out it may be causing bias on indexes commonly used to determine the impact of a journal (Goldberg et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

“…There are different indicators to indicate the degree of influence of a magazine or paper in a particular field of study. Commonly used are the inclusion of the journal in prestigious indexing databases such as the Web of Science (Merigó, Gil-Lafuente, & Yager, 2015), their annual impact factor (Garfield, 1972;Glanzer & Moed, 2002) and 5-year impact factor (Amin & Mabe, 2003;Campanario, 2011), the number of papers published in a period of time (Goldberg, Anthony, & Evans, 2015), the number of citations received (Podsakoff, MacKenzie, Podsakoff, & Bachrach, 2008), the h-index (Hirsch, 2005;Hirsch & Buela-Casal, 2014), the ratio cites/paper (Merigó, Mas-Tur, Roig-Tierno, & Ribeiro-Soriano, 2015), or the ratio papers/year (Goldberg et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

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Twenty-five years of research on work and organizational psychology: A bibliometric perspective

Romeo

Yepes-Baldó

Boria‐Reverter

et al. 2017

Anuario de Psicología

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The research aims to analyze the scientific productivity in the field of work/organizational psychology (WOP) in the last 25 years. We focus our analysis on the most influential journals and articles, generally and for 5-year periods, as well as structures of co-citation among the highest quality journals based on their h-index. We found that a high percentage of papers published each year receive between 5 and 10 cites. Secondly, we observe an exponential increase in the number of papers published, citations, and h-index. Additionally, the number of self-citations significantly increases in the last 5 years. In this sense, we consider that the most recent papers need more time to increase their level of citation and, subsequently, to correct the bias on self-citation. This research shows the status of research in the field of work/organizational psychology, analyzing the scientific journals and papers published in the Web of Science. © 2017 Universitat de Barcelona. Published by Elsevier España, S.L.U. All rights reserved. PALABRAS CLAVEPsicología del trabajo y de las organizaciones; Índice h; Citación; Bibliometría Veinticinco años de investigación en psicología del trabajo y de las organizaciones: una perspectiva bibliométrica Resumen La presente investigación tiene como objetivo analizar la producción científica en el ámbito de la psicología del trabajo y las organizaciones en los últimos 25 años. Centramos nuestro análisis en las revistas y artículos con el nivel más alto de índice h, en general y por períodos de 5 años, así como en las estructuras de cocitación entre ellas. Los resultados muestran que un alto porcentaje de artículos reciben entre 5 y 10 citas. En segundo lugar, se observa un aumento exponencial en el número de trabajos publicados, las citas, así como el * Corresponding author. E-mail address: mromeo@ub.edu (M. Romeo). Twenty-five years of research on WOP 33 índice h. Además, el número de autocitas aumenta significativamente en los trabajos publicados en los últimos 5 años. En este sentido, consideramos que los documentos más recientes necesitan más tiempo para aumentar su nivel de citación y, posteriormente, para corregir el sesgo de autocita. La presente investigación muestra el estado de la investigación en el ámbito de la psicología del trabajo y de las organizaciones a partir del análisis de las revistas y artículos científicos publicados en la Web of Science.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Twenty-five years of research on work and organizational psychology: A bibliometric perspective

Romeo

Yepes-Baldó

Boria‐Reverter

et al. 2017

Anuario de Psicología

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“…Association networks are found in many domains, such as networks of citation patterns across scientific articles [1][2][3], networks of actors co-starring in movies [4][5][6], networks of regulatory influence among genes [7,8], and networks of functional connectivity between regions of the brain [9,10]. The rules defining edges in association networks are not the same.…”

Section: Problem Statementmentioning

confidence: 99%

Inferring Weighted Directed Association Networks from Multivariate Time Series with the Small-Shuffle Symbolic Transfer Entropy Spectrum Method

Zhao

2016

Entropy

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Abstract:Complex network methodology is very useful for complex system exploration. However, the relationships among variables in complex systems are usually not clear. Therefore, inferring association networks among variables from their observed data has been a popular research topic. We propose a method, named small-shuffle symbolic transfer entropy spectrum (SSSTES), for inferring association networks from multivariate time series. The method can solve four problems for inferring association networks, i.e., strong correlation identification, correlation quantification, direction identification and temporal relation identification. The method can be divided into four layers. The first layer is the so-called data layer. Data input and processing are the things to do in this layer. In the second layer, we symbolize the model data, original data and shuffled data, from the previous layer and calculate circularly transfer entropy with different time lags for each pair of time series variables. Thirdly, we compose transfer entropy spectrums for pairwise time series with the previous layer's output, a list of transfer entropy matrix. We also identify the correlation level between variables in this layer. In the last layer, we build a weighted adjacency matrix, the value of each entry representing the correlation level between pairwise variables, and then get the weighted directed association network. Three sets of numerical simulated data from a linear system, a nonlinear system and a coupled Rossler system are used to show how the proposed approach works. Finally, we apply SSSTES to a real industrial system and get a better result than with two other methods.

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“…Association networks are found in many domains, such as networks of citation patterns across scientific articles [1–3], networks of actors co-starring in movies [4–6], networks of regulatory influence among genes [7, 8], and networks of functional connectivity between regions of the brain[9, 10]. The rules defining edges in association networks are not the same.…”

Section: Introductionmentioning

confidence: 99%

Inferring Weighted Directed Association Network from Multivariate Time Series with a Synthetic Method of Partial Symbolic Transfer Entropy Spectrum and Granger Causality

Zhao

2016

PLoS ONE

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Complex network methodology is very useful for complex system explorer. However, the relationships among variables in complex system are usually not clear. Therefore, inferring association networks among variables from their observed data has been a popular research topic. We propose a synthetic method, named small-shuffle partial symbolic transfer entropy spectrum (SSPSTES), for inferring association network from multivariate time series. The method synthesizes surrogate data, partial symbolic transfer entropy (PSTE) and Granger causality. A proper threshold selection is crucial for common correlation identification methods and it is not easy for users. The proposed method can not only identify the strong correlation without selecting a threshold but also has the ability of correlation quantification, direction identification and temporal relation identification. The method can be divided into three layers, i.e. data layer, model layer and network layer. In the model layer, the method identifies all the possible pair-wise correlation. In the network layer, we introduce a filter algorithm to remove the indirect weak correlation and retain strong correlation. Finally, we build a weighted adjacency matrix, the value of each entry representing the correlation level between pair-wise variables, and then get the weighted directed association network. Two numerical simulated data from linear system and nonlinear system are illustrated to show the steps and performance of the proposed approach. The ability of the proposed method is approved by an application finally.

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Modelling citation networks

Cited by 44 publications

References 49 publications

Twenty-five years of research on work and organizational psychology: A bibliometric perspective

Twenty-five years of research on work and organizational psychology: A bibliometric perspective

Inferring Weighted Directed Association Networks from Multivariate Time Series with the Small-Shuffle Symbolic Transfer Entropy Spectrum Method

Inferring Weighted Directed Association Network from Multivariate Time Series with a Synthetic Method of Partial Symbolic Transfer Entropy Spectrum and Granger Causality

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