Kijung Yoon

861 total citations
13 papers, 476 citations indexed

About

Kijung Yoon is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Epidemiology. According to data from OpenAlex, Kijung Yoon has authored 13 papers receiving a total of 476 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Cognitive Neuroscience, 3 papers in Cellular and Molecular Neuroscience and 3 papers in Epidemiology. Recurrent topics in Kijung Yoon's work include Memory and Neural Mechanisms (3 papers), Stress Responses and Cortisol (2 papers) and Neural dynamics and brain function (2 papers). Kijung Yoon is often cited by papers focused on Memory and Neural Mechanisms (3 papers), Stress Responses and Cortisol (2 papers) and Neural dynamics and brain function (2 papers). Kijung Yoon collaborates with scholars based in South Korea, United States and Australia. Kijung Yoon's co-authors include Ila Fiete, Neil Burgess, Michael A. Buice, Robin Hayman, Caswell Barry, Seungho Ryu, Yoosoo Chang, Amina A. Kinkhabwala, David W. Tank and Sam Lewallen and has published in prestigious journals such as Cell, Neuron and Nature Neuroscience.

In The Last Decade

Kijung Yoon

13 papers receiving 463 citations

Peers

Kijung Yoon

CMN

EPIDE

EDM

NEPHR

Caitlin Monaghan United States

Jack S. Fogarty Australia

Sanne K. Meles Netherlands

Mikel Tainta Spain

Yu‐Tai Tsai Taiwan

Takami Yagyu Japan

Antonio Eblén-Zajjur Venezuela

Mitchell H. Parks United States

Carlos Amaral Portugal

Caitlin Monaghan United States

Kijung Yoon

290 ×1.1

213 ×1.0

CMN

9 ×0.1

EPIDE

4 ×0.1

EDM

30 ×0.6

NEPHR

Citations per year, relative to Kijung Yoon Kijung Yoon (= 1×) peers Caitlin Monaghan

Countries citing papers authored by Kijung Yoon

Since Specialization

Citations

This map shows the geographic impact of Kijung Yoon's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kijung Yoon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kijung Yoon more than expected).

Fields of papers citing papers by Kijung Yoon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kijung Yoon. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kijung Yoon. The network helps show where Kijung Yoon may publish in the future.

Co-authorship network of co-authors of Kijung Yoon

This figure shows the co-authorship network connecting the top 25 collaborators of Kijung Yoon. A scholar is included among the top collaborators of Kijung Yoon based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kijung Yoon. Kijung Yoon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

Kim, Youngjin, et al.. (2024). Electrical synaptic devices with a high recognition rate based on eco-friendly nanocomposites of a poly(methyl methacrylate) matrix embedded with graphene quantum dots for neuromorphic computing. Organic Electronics. 126. 106997–106997. 3 indexed citations

Yoon, Kijung, et al.. (2023). Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting. IEEE Access. 11. 118386–118394. 5 indexed citations

Yoon, Kijung, et al.. (2022). Two-Argument Activation Functions Learn Soft XOR Operations Like Cortical Neurons. IEEE Access. 10. 58071–58080. 1 indexed citations

Yoon, Kijung, et al.. (2021). Degree Matters: Assessing the Generalization of Graph Neural Network. 1 indexed citations

Yoon, Kijung, et al.. (2018). Higher and increased concentration of hs-CRP within normal range can predict the incidence of metabolic syndrome in healthy men. Diabetes & Metabolic Syndrome Clinical Research & Reviews. 12(6). 977–983. 19 indexed citations

Gu, Yi, Sam Lewallen, Amina A. Kinkhabwala, et al.. (2018). A Map-like Micro-Organization of Grid Cells in the Medial Entorhinal Cortex. Cell. 175(3). 736–750.e30. 60 indexed citations

Yoon, Kijung, et al.. (2017). High-normal levels of hs-CRP predict the development of non-alcoholic fatty liver in healthy men. PLoS ONE. 12(2). e0172666–e0172666. 58 indexed citations

Yoon, Kijung, Sam Lewallen, Amina A. Kinkhabwala, David W. Tank, & Ila Fiete. (2016). Grid Cell Responses in 1D Environments Assessed as Slices through a 2D Lattice. Neuron. 89(5). 1086–1099. 45 indexed citations

Yoon, Kijung, Michael A. Buice, Caswell Barry, et al.. (2013). Specific evidence of low-dimensional continuous attractor dynamics in grid cells. Nature Neuroscience. 16(8). 1077–1084. 180 indexed citations

10.

Lee, Kyoungho, Kijung Yoon, Mina Ha, et al.. (2010). Heart Rate Variability and Urinary Catecholamines from Job Stress in Korean Male Manufacturing Workers According to Work Seniority. Industrial Health. 48(3). 331–338. 13 indexed citations

11.

Lee, Eunil, Daehee Kang, Kijung Yoon, et al.. (2010). A pilot study on the association between job stress and repeated measures of immunological biomarkers in female nurses. International Archives of Occupational and Environmental Health. 83(7). 779–789. 18 indexed citations

12.

Ryu, Seungho, Yoosoo Chang, Hee‐Yeon Woo, et al.. (2008). Time-Dependent Association Between Metabolic Syndrome and Risk of CKD in Korean Men Without Hypertension or Diabetes. American Journal of Kidney Diseases. 53(1). 59–69. 70 indexed citations

13.

Yoon, Kijung & Richard Coyne. (1992). Reasoning about spatial constraints. Environment and Planning B Planning and Design. 19(3). 243–266. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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