Analysis of Research Trends in the Field of Museology: Application of Node2Vec and Referential Network Modelling
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Keywords

Bibliometrics
Museology
Citation Network Analysis
Node2Vec
Scientometrics

DOI

10.26689/erd.v7i6.11035

Submitted : 2025-06-07
Accepted : 2025-06-22
Published : 2025-07-07

Abstract

This study uses the Node2Vec network embedding technology combined with citation network modelling to systematically analyze the knowledge evolution in the research field of museology. Based on 6,726 relevant documents included in the Scopus database from 1948 to 2023, this research constructs a high-dimensional citation network and applies cluster analysis and regression modelling to explore the theme development trends, core research themes, and their influence in this field. The research finds that museology research mainly focuses on cultural heritage protection, digital technology applications, museum education, and public participation, and has shown a trend of interdisciplinary integration in recent years. In addition, with the help of IPY (Intrinsic Publication Year) analysis, this study reveals the inter-generational evolution of research hotspots and their high synchronization with policy revisions and technological innovations (such as the rise of augmented reality technology). The research shows that the knowledge diffusion model of modern research has shifted from traditional collection management to digital-based knowledge sharing and social practice. Finally, this study suggests that future academic research can combine Temporal Graph Attention Networks (TGAT) to improve the representational ability of early literature and multilingual knowledge flows to comprehensively understand the disciplinary development path of museology.

References

Duff W, Carter J, Cherry J, 2013, Archival Education and the Need for Cultural Heritage Professionals. Journal of Archival Organization, 11(3–4): 173–208.

Hider P, Kennan M, 2020, Relationships Between Library and Information Science and Museum Studies. Journal of the Association for Information Science and Technology, 71(4): 405–417.

Kim Y, 2012, Integrating Museum Studies Into LIS Curricula. Journal of Education for Library and Information Science, 53(2): 101–115.

Latham K, Simmons J, 2019, Defining the Museum: Past, Present, and Future. Museum Management and Curatorship, 34(5): 453–467.

Sigfúsdóttir I, 2020, Cross-Disciplinary Approaches to Museum Studies: An Analysis. Museum International, 72(1–2): 68–77.

Waibel G, Erway R, 2009, Think Global, Act Local: Library, Archive, and Museum Collaboration. Library Trends, 57(3): 519–527.

Chen C, 2006, CiteSpace II: Detecting and Visualizing Emerging Trends and Transient Patterns in Scientific Literature. Journal of the American Society for Information Science and Technology, 57(3): 359–377.

Chen C, Song M, 2017, Representing Scientific Knowledge: The Role of Citation Analysis. Scientometrics, 111(2): 1527–1542.

Grover A, Leskovec J, 2016, Node2Vec: Scalable Feature Learning for Networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016: 855–864.

Perozzi B, Al-Rfou R, Skiena S, 2014, DeepWalk: Online Learning of Social Representations. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014: 701–710.

Xia W, Li T, Li C, 2023, A Review of Scientific Impact Prediction: Tasks, Features and Methods. Scientometrics, 128(1): 543–585.

Geng Y, Zhang X, Gao J, et al., 2024, Bibliometric Analysis of Sustainable Tourism Using CiteSpace. Technological Forecasting and Social Change, 2024, 202: 123310.

Wang S, Chen Y, Lv X, et al., 2023, Hot Topics and Frontier Evolution of Science Education Research: A Bibliometric Mapping From 2001 to 2020. Science & Education, 32(3): 845–869.

Liu S, Pan Y, 2023, Exploring Trends in Intangible Cultural Heritage Design: A Bibliometric and Content Analysis. Sustainability, 15(13): 10049.

Hou Y, Xu L, Chen L, 2022, Hotspots and Cutting‐Edge Visual Analysis of Digital Museum in China Using Data Mining Technology. Computational Intelligence and Neuroscience, 2022(1): 7702098.