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Purpose: The purpose of this descriptive study was to develop a knowledge map through a social network analysis of dementia-related articles. Methods: For 276,548 keywords from 52,225 studies on dementia, co-occurrence keywords were used to analyze the node centrality in terms of degree, closeness, betweenness, and cohesion through social network analysis. Co-occurrence words were presented using word clouds. Results: The keywords Parkinson disease, amyloid, Mild Cognition Impairment (MCI), Apolipoprotein E (ApoE), depression, tau, aged, neuropsychology, memory, and neurodegeneration demonstrated high degree centrality with regard to dementia and Alzheimer disease. When central keywords such as dementia and Alzheimer disease were excluded, aged, depression, tau, memory, cognition, MCI, Frontotemporal Dementia (FTD), cognition impairment, Parkinson disease, and diagnosis showed high degree centrality. Conclusion: The findings from this study help identify research trends and topics. They also provide information regarding various fields of research by analyzing cohesion between keywords that enable research exchange or convergence, and keywords that promote or control interaction with other keywords. This analysis can help establish research topics at the initial stages and guide researchers when identifying and selecting important nursing science topics related to dementia.

Social Network Analysis on Mapping the Knowledge Structure of Dementia Research