Deep graph learning for anomalous citation detection

Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to scholarly data, that is, anomaly detection in a citation network. Citation is considered as one of the most crucial metrics to evaluate the impact of scientific research, which may be gamed in multiple ways. Therefore, anomaly detection in citation networks is of significant importance to identify manipulation and inflation of citations. To address this open issue, we propose a novel deep graph learning model, namely graph learning for anomaly detection (GLAD), to identify anomalies in citation networks. GLAD incorporates text semantic mining to network representation learning by adding both node attributes and link attributes via graph neural networks (GNNs). It exploits not only the relevance of citation contents, but also hidden relationships between papers. Within the GLAD framework, we propose an algorithm called Citation PUrpose (CPU) to discover the purpose of citation based on citation context. The performance of GLAD is validated through a simulated anomalous citation dataset. Experimental results demonstrate the effectiveness of GLAD on the anomalous citation detection task. © 2012 IEEE

Los Journal Citation Reports (edición SCI) con y sin autocitas de revista

Title: The Journal Citation Reports (SCI edition) with and without journal selfcitations. Journals are ranked in the subject categories of the Journal Citation Reports (JCR) according to their impact factors (JIF). The calculation of this indicator includes both citations received from other journals and self-citations from the journal itself. In this paper the current model of the JCR is compared with an alternate model using the JIF without self-citations as a variable. In order to do this, simulations were carried out using JIF without self-citations as an alternative variable. We studied the relationship between the two variables (JIF with self-citations and JIF without self-citations), the gain obtained by the journals with the usual JIF with self-citations, the changes in the ranking in the whole JCR, and changes in the internal ranking in the JCR’s subject categories. A small negative correlation was found between the gain that the journals obtained and their JIF. The results obtained suggest that if JIF without self-citations were used as the variable, the ranking of journals in the JCR and within the subject categories would be, in general, very different from the current one. The quantification of these differences is one of the more interesting results of this work

Los Journal Citation Reports (edición SCI) con y sin autocitas de revista

Title: The Journal Citation Reports (SCI edition) with and without journal selfcitations. Journals are ranked in the subject categories of the Journal Citation Reports (JCR) according to their impact factors (JIF). The calculation of this indicator includes both citations received from other journals and self-citations from the journal itself. In this paper the current model of the JCR is compared with an alternate model using the JIF without self-citations as a variable. In order to do this, simulations were carried out using JIF without self-citations as an alternative variable. We studied the relationship between the two variables (JIF with self-citations and JIF without self-citations), the gain obtained by the journals with the usual JIF with self-citations, the changes in the ranking in the whole JCR, and changes in the internal ranking in the JCR’s subject categories. A small negative correlation was found between the gain that the journals obtained and their JIF. The results obtained suggest that if JIF without self-citations were used as the variable, the ranking of journals in the JCR and within the subject categories would be, in general, very different from the current one. The quantification of these differences is one of the more interesting results of this work

Autocitas de revistas incluidas en una misma categoría temática del Clarivate Journal Citation Reports (JCR): el caso de la revista Adicciones

The objective of this study is to determine if the criteria imposed by Clarivate Analytics regarding the penalization of journals that have an elevated number of self-citations should be applied to all journals equally, without considering differential bibliometric indicators between journals. The editorial characteristics of the journal Adicciones, which was excluded from JCR in 2019 due to an excess of self-citations, were analyzed opposite journals of its same category, and different bibliometric indicators of the journal Adicciones were analyzed as well. The results indicate different editorial characteristics between publications included in the category of Substance Abuse in the JCR according to country of editing, number of thematic categories, publication language, edited numbers per year and editorial. The existence of a majority presence of a single country is observed in the journal Adicciones (Spanish authors in both published and cited articles). It can be concluded that the criteria followed by Clarivate Analytics regarding journal self-citations requires a review attending to the particular characteristics of each scientific publication.El objetivo del presente estudio es determinar si el criterio impuesto por Clarivate Analytics de penalizar las revistas que tengan un elevado número de autocitas debería aplicarse a todas las revistas por igual, sin considerar indicadores bibliométricos diferenciales entre revistas. Se analizaron las características editoriales de la revista Adicciones, la cual fue excluida del JCR del 2019 por un exceso de autocitas, frente a las revistas de su misma categoría, y diferentes indicadores bibliométricos de la revista Adicciones. Los resultados indican características editoriales diferentes entre las publicaciones incluidas en la categoría Substance Abuse del JCR según país de edición, número de categorías temáticas, idioma de publicación, números editados al año y editorial. Se observa que en la revista Adicciones existe una presencia muy mayoritaria de un único país (autores españoles) tanto en los artículos publicados como en los citados. Se puede concluir que el criterio seguido por Clarivate Analytics con respecto a las autocitas de revista requiere de una revisión atendiendo a las características concretas de cada publicación científica

Autocitas de revistas incluidas en una misma categoría temática del Clarivate Journal Citation Reports (JCR): el caso de la revista Adicciones

El objetivo del presente estudio es determinar si el criterio impuesto por Clarivate Analytics de penalizar las revistas que tengan un elevado número de autocitas debería aplicarse a todas las revistas por igual, sin considerar indicadores bibliométricos diferenciales entre revistas. Se analizaron las características editoriales de la revista Adicciones, la cual fue excluida del JCR del 2019 por un exceso de autocitas, frente a las revistas de su misma categoría, y diferentes indicadores bibliométricos de la revista Adicciones. Los resultados indican características editoriales diferentes entre las publicaciones incluidas en la categoría Substance Abuse del JCR según país de edición, número de categorías temáticas, idioma de publicación, números editados al año y editorial. Se observa que en la revista Adicciones existe una presencia muy mayoritaria de un único país (autores españoles) tanto en los artículos publicados como en los citados. Se puede concluir que el criterio seguido por Clarivate Analytics con respecto a las autocitas de revista requiere de una revisión atendiendo a las características concretas de cada publicación científica