This study presents a novel methodology for analyzing disease relationships from a network perspective using Large Language Model (LLM) embeddings. We constructed a disease network based on 4,489 diseases from the International Classification of Diseases (ICD-11) using OpenAI’s text-embedding-3-small model. Network analysis revealed that diseases exhibit small-world characteristics with a high clustering coefficient (0.435) and form 16 major communities. Notably, mental health-related diseases showed high centrality in the network, and a clear inverse relationship was observed between community size and internal density. The embedding-based relationship analysis revealed meaningful patterns of disease relationships, suggesting the potential of this methodology as a novel tool for studying disease associations. Results suggest that mental health conditions play a more central role in disease relationships than previously recognized, and disease communities show distinct organizational patterns. This approach shows promise as a valuable tool for exploring large-scale disease relationships and generating new research hypotheses.

This study aims to collect and analyze Common European Framework of Reference for Languages (CEFR)-related research in Korean language education to identify emerging trends. It examines 28 academic articles published in Korea from 2020 to 2024, using text mining and language network analysis methods. Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) analyses revealed that studies on curriculum design and application in Korean language education appeared with high frequency. Semantic network analysis identified key research directions, such as comparing proficiency level systems in Korean curricula, proposing “mediation” activities based on CEFR, and evaluating CEFR as an assessment tool. Latent Dirichlet Allocation (LDA) topic modeling categorized the studies into three groups: (1) research directly analyzing CEFR, (2) research applying CEFR to overseas Korean language curriculum design, and (3) research comparing existing Korean curricula with CEFR. This study is significant as the first to analyze CEFR-related research trends in Korean language education. By employing objective data analysis tools such as text mining, it enhances the reliability of findings and provides valuable insights into recent research trends.

The purpose of this study is to analyze research trends in Korean language education for North Korean refugees. In pursuance of this goal, the study collected bibliographic information from 1,924 academic papers related to North Korean defectors and analyzed their research trends using language network analysis methods. The frequency and centrality of the academic papers were analyzed by year, using an analysis tool, NetMiner 4.0, which focuses on analyzing social networks. The findings of the analysis were as follows: First, the study of North Korean defectors began to explode in 2010. Second, the most central words were ‘education,’ ‘unification,’ ‘policy,’ ‘support,’ ‘experience’ and ‘relationship.’ Third, for North Korean defectors, Korean language education as a foreign language was more actively done than general Korean language education. Fourth, the analysis of 15 topics showed that topics on social issues accounted for the highest percentage at 25%. Finally, the areas of greatest interest in Korean language education were vocabulary, pronunciation, and intonation education. It is hoped that more research on Korean language education for North Korean defectors will be carried out in the future.

Research on cosmetic behavior has developed significantly since the 2000s. Reviewing cosmetic behavior research can be meaningful because it can grasp trends in the domestic cosmetics market, and it can also illuminate how domestic consumers’ interest in makeup has changed over time. The purpose of this study is to investigate the links between major keywords and the keywords which affect makeup behavior of different age groups through network analysis. In this study we analyzed thesis and journal data based on makeup behavior through network analysis using Nodexl. We analyzed 10 years of journals and theses - from 2000 to 2017, and investigated age-related differences in variables related to makeup behavior. Research subjects were divided into age-based groups: 10, 20-40, and over 50. The total number of theses collected was 82. In order to perform network analysis using the Nodexl program, we extracted the frequency of representative words using the KrKwic program. The extracted core words were analyzed for degree centrality, betweenness centrality and eigenvector centrality using Nodexl. The expected result is that the network analysis using keywords will lead to different variables depending on age and the main goal of the cosmetics market, and it is expected to be used as the basis for follow-up research related to cosmetic behavior.

본고는 국어 진로 선택 과목을 탐색하는 것을 목적으로, 국어 관련 직업과 직업에서 많이 요구 하는 세부 전공 요건을 조사하여 네트워크 분석을 시행한 후 그 결과를 제시하였다. 그 결과 “광고언어학”, “언어와 시각 예술” 요건이 연결 중앙성 값이 높게 나타나 많은 국어 관련 직업에서 동시에 요구하는 요건인 것으로 파악되었고, “광고언어학”과 “신문방송학”이 사이 중앙성 값이 높게 나타나 국어 관련 직업들을 서로 이어주는 데에 주요한 역할을 하는 요건인 것으로 파악되었다. 따라서 이들 내용과 관련한 과목을 국어과 진로 선택 과목을 신설하는 방안을 제안하였다. 본고의 연구 결과는 선택 과목이 갖추어야 할 ‘위계성’, ‘총체성’, ‘실제성’, ‘완결성’을 다각도로 고려하지는 못하였다는 한계를 가지지만, 국어 진로 선택 과목을 탐색하기 위한 초기 연구로서, 추후 본격적인 국어 진로 선택 과목을 개발하는 데에 기초 자료로 활용될 수 있기를 기대한다.