Discovering the role model of authors by embedding research history

Jeon, Hyeon-Ju; Jung, Jason J.

doi:10.1177/01655515211034407

Cited by 3 publications

(3 citation statements)

References 53 publications

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“…Our study proposes a novel approach using Graph Neural Networks (GNNs) [7,8,9] to estimate the impact of observations independently of the system's structure. GNNs have been increasingly employed in meteorological predictions, including solar radiation and sea surface temperature predictions, by capturing variable interactions in neighboring regions [10,11,12].…”

Section: Introductionmentioning

confidence: 99%

JEON, Beomseok (BJ): Seoul/Republic of Korea

Jeon

2019

Leadership in Movement Disorders

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This paper investigates the impact of observations on atmospheric state estimation in weather forecasting systems using graph neural networks (GNNs) and explainability methods. We integrate observation and Numerical Weather Prediction (NWP) points into a meteorological graph, extracting k-hop subgraphs centered on NWP points. Self-supervised GNNs are employed to estimate the atmospheric state by aggregating data within these k-hop radii. The study applies gradient-based explainability methods to quantify the significance of different observations in the estimation process. Evaluated with data from 11 satellite and land-based observations, the results highlight the effectiveness of visualizing the importance of observation types, enhancing the understanding and optimization of observational data in weather forecasting.

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Section: Introductionmentioning

confidence: 99%

JEON, Beomseok (BJ): Seoul/Republic of Korea

Jeon

2019

Leadership in Movement Disorders

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“…However, the population of patients with allergic diseases in each region exhibits irregularly structured data, similar to gene interaction networks and chemical molecular structures, which means that they are graph-structured data rather than regular grid data to which CNNs can be applied. Recently, graph convolutional networks (GCN) [ 35 – 37 ] have been used to capture the structural features of graphs for various tasks, including medical diagnoses. A few studies [ 38 ] have conducted disease prediction using the GCN model, which considers comorbidity relationships among diseases, to discover new disease correlations among their study cohorts.…”

Section: Methodsmentioning

confidence: 99%

Predicting the daily number of patients for allergic diseases using PM10 concentration based on spatiotemporal graph convolutional networks

Jeon,

Jeon

2024

PLoS ONE

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Air pollution causes and exacerbates allergic diseases including asthma, allergic rhinitis, and atopic dermatitis. Precise prediction of the number of patients afflicted with these diseases and analysis of the environmental conditions that contribute to disease outbreaks play crucial roles in the effective management of hospital services. Therefore, this study aims to predict the daily number of patients with these allergic diseases and determine the impact of particulate matter (PM10) on each disease. To analyze the spatiotemporal correlations between allergic diseases (asthma, atopic dermatitis, and allergic rhinitis) and PM10 concentrations, we propose a multi-variable spatiotemporal graph convolutional network (MST-GCN)-based disease prediction model. Data on the number of patients were collected from the National Health Insurance Service from January 2013 to December 2017, and the PM10 data were collected from Airkorea during the same period. As a result, the proposed disease prediction model showed higher performance (R2 0.87) than the other deep-learning baseline methods. The synergic effect of spatial and temporal analyses improved the prediction performance of the number of patients. The prediction accuracies for allergic rhinitis, asthma, and atopic dermatitis achieved R2 scores of 0.96, 0.92, and 0.86, respectively. In the ablation study of environmental factors, PM10 improved the prediction accuracy by 10.13%, based on the R2 score.

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“…The publication trajectory over the course of a researcher's career has been studied in the literature, with a common focus on the performance patterns in terms of publication counts or received citations 1 , 10 – 13 . While these indicators (e.g., publications and citations) have been used as the most prominent basis for tracking the author's career profile, there have also been attempts to understand the publication trajectory that incorporates more fine-grained information, such as the research topic, author affiliation, collaborator, or bibliographic network 14 – 19 . For example, a recent study explores the mobility of researchers across research topics.…”

Section: Introductionmentioning

confidence: 99%

Understanding scholar-trajectories across scientific periodicals

Fan,

Blok,

Lehmann

2024

Sci Rep

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Despite the rapid growth in the number of scientific publications, our understanding of author publication trajectories remains limited. Here we propose an embedding-based framework for tracking author trajectories in a geometric space that leverages the information encoded in the publication sequences, namely the list of the consecutive publication venues for each scholar. Using the publication histories of approximately 30,000 social media researchers, we obtain a knowledge space that broadly captures essential information about periodicals as well as complex (inter-)disciplinary structures of science. Based on this space, we study academic success through the prism of movement across scientific periodicals. We use a measure from human mobility, the radius of gyration, to characterize individual scholars' trajectories. Results show that author mobility across periodicals negatively correlates with citations, suggesting that successful scholars tend to publish in a relatively proximal range of periodicals. Overall, our framework discovers intricate structures in large-scale sequential data and provides new ways to explore mobility and trajectory patterns.

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Discovering the role model of authors by embedding research history

Cited by 3 publications

References 53 publications

JEON, Beomseok (BJ): Seoul/Republic of Korea

JEON, Beomseok (BJ): Seoul/Republic of Korea

Predicting the daily number of patients for allergic diseases using PM10 concentration based on spatiotemporal graph convolutional networks

Understanding scholar-trajectories across scientific periodicals

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