Graph Neural Networks: A Bibliometric Mapping of the Research Landscape and Applications DOI Creative Commons
Annielle Mendes Brito da Silva, Natiele Carla da Silva Ferreira, Luiza Amara Maciel Braga

et al.

Information, Journal Year: 2024, Volume and Issue: 15(10), P. 626 - 626

Published: Oct. 11, 2024

Graph neural networks (GNNs) are deep learning algorithms that process graph-structured data and suitable for applications such as social networks, physical models, financial markets, molecular predictions. Bibliometrics, a tool tracking research evolution, identifying milestones, assessing current research, can help identify emerging trends. This study aims to map GNN applications, directions, key contributors. An analysis of 40,741 GNN-related publications from the Web Science Core Collection reveals rising trend in publications, especially since 2018. Computer Science, Engineering, Telecommunications play significant roles with focus on learning, graph convolutional machine learning. China USA combined account 76.4% publications. Chinese universities concentrate feature extraction, task analysis, whereas American The also highlights importance Chemistry, Physics, Mathematics, Imaging & Photographic Technology, their respective knowledge communities. In conclusion, bibliometric provides an overview showing growing interest across various disciplines, highlighting potential GNNs solving complex problems need continued collaboration.

Language: Английский

Mapping Fifteen Years of Technological Pedagogical and Content Knowledge (TPACK) Model Applications in Higher Education DOI Creative Commons
Rong Zou,

Leilei Jiang,

Yabing Cao

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 21, 2024

Abstract This study presents a bibliometric analysis of the Technological Pedagogical Content Knowledge (TPACK) model’s evolution in higher education from 2007 to 2023, with 332 publications WoS. Employing co-citation, bibliographic coupling, and co-word analyses, it examines key trends, thematic clusters, role professional development institutional support TPACK integration. Results indicate that while TPACK’s foundational components—technology, pedagogy, content—are essential, recent trends emphasize adaptability, self-efficacy, digital competence as crucial for effective technology use. The COVID-19 pandemic further accelerated adoption, underscoring importance hybrid learning models targeted training educators. highlights areas concentrated research within identifying gaps could inform future studies, providing insights educators, researchers, policymakers aiming foster technology-enhanced teaching. Future directions suggest integrating emerging technologies like AI virtual reality into exploring region-specific factors affecting adoption educational contexts.

Language: Английский

Citations

0
Intervention Effects of Three Subtypes of ADHD: Neurofeedback Therapy and Exercise Therapy DOI

炳秋 王

Advances in Psychology, Journal Year: 2024, Volume and Issue: 14(12), P. 267 - 276

Published: Jan. 1, 2024

Language: Английский

Citations

0
Burnout in humanitarian work: A qualitative study on the life experiences of workers in Malaysia DOI Open Access

Sue Jen Cheong,

Ng Chong Guan, Wendy Diana Shoesmith

et al.

Journal of Infrastructure Policy and Development, Journal Year: 2024, Volume and Issue: 8(8), P. 4632 - 4632

Published: Aug. 13, 2024

Humanitarian workers face numerous challenges when providing assistance to people affected by natural disasters, armed conflicts, and other crises, which often leads burnout psychological distress. This qualitative study investigates the interplay of factors that contribute among Malaysian employees a refugee-focused humanitarian organization. Ten staff members participated in focus group discussions, revealed five themes: positive meaningful emotions; difficult negative vicarious trauma, stress, burnout; work environment, culture, managerial policies; structural governmental stressors. The emphasizes need for improved support resources workers, as well enhanced organizational policies practices prevent mitigate burnout. findings suggest culturally adapted interventions, such Acceptance Commitment Therapy (ACT), can help address their unique challenges. More research is needed examine issues present within organizations using methods adapt appropriate interventions development psychopathology these settings.

Language: Английский

Citations

0
Graph Neural Networks: A Bibliometric Mapping of the Research Landscape and Applications DOI Creative Commons
Annielle Mendes Brito da Silva, Natiele Carla da Silva Ferreira, Luiza Amara Maciel Braga

et al.

Information, Journal Year: 2024, Volume and Issue: 15(10), P. 626 - 626

Published: Oct. 11, 2024

Graph neural networks (GNNs) are deep learning algorithms that process graph-structured data and suitable for applications such as social networks, physical models, financial markets, molecular predictions. Bibliometrics, a tool tracking research evolution, identifying milestones, assessing current research, can help identify emerging trends. This study aims to map GNN applications, directions, key contributors. An analysis of 40,741 GNN-related publications from the Web Science Core Collection reveals rising trend in publications, especially since 2018. Computer Science, Engineering, Telecommunications play significant roles with focus on learning, graph convolutional machine learning. China USA combined account 76.4% publications. Chinese universities concentrate feature extraction, task analysis, whereas American The also highlights importance Chemistry, Physics, Mathematics, Imaging & Photographic Technology, their respective knowledge communities. In conclusion, bibliometric provides an overview showing growing interest across various disciplines, highlighting potential GNNs solving complex problems need continued collaboration.

Language: Английский

Citations

0