Symmetric and Asymmetric Trends in Sparse Tucker Decomposition Based on Plithogenic Neutrosophic Hypersoft Sets: A Bibliometric and Conceptual Review

Sparse Tucker decomposition (STD) is a framework for tensor decomposition that extends the classical Tucker model with sparsity constraints, with the aim of achieving better representation and analysis of multidimensional data. STD has attracted growing interest from various scientific fields, such as neuroimaging, machine learning, telecommunications, statistical computing, and brain connectivity analysis, due to its flexibility and computational efficiency. The purpose of this study is to present a systematic and structured review of the scientific literature on STD, describing its main trends, applications, and the evolution of research over time. Methods: A systematic review was conducted in accordance with the PRISMA 2020 guidelines using the Scopus and Web of Science databases. The search retrieved 21 records, of which 13 were selected for detailed analysis according to the established inclusion criteria. To improve the evaluation process, the selected studies were assessed using the plithogenic neutrosophic hypersoft set framework, which allowed for the evaluation of scientific contributions in terms of degrees of truth, indeterminacy, and falsity. The evaluation covered aspects such as methodological rigor, citation impact, thematic relevance, journal quality, and international collaboration networks. Results: Research on STD spans 2013 to 2025—the year of the first indexed record through the search date—and shows a marked increase after 2021. Five main application domains were identified: computational optimization, neuroimaging and neuropsychology, telecommunications, statistical analysis, and studies on brain connectivity. The literature on STD is still relatively limited and geographically concentrated, especially in the United States and China, despite the strong interdisciplinary potential and the growing scientific relevance of STD. Conclusions: STD is symmetric by construction, treating every tensor mode and component equally and applying the same sparsity penalty throughout, yet the literature surrounding it is markedly uneven, being concentrated in a few countries and a small number of technical fields. The PNHS framework allowed us to characterize this mismatch while accounting for the uncertainty of each judgement, scoring productivity, impact, collaboration, and thematic relevance through separate degrees of truth, indeterminacy, and falsity. This study identifies STD as a promising interdisciplinary analytical framework and demonstrates the value of plithogenic neutrosophic hypersoft methodologies for evaluating the scientific literature under uncertainty and multidimensionality. The review emphasizes the need for greater methodological standardization, increased international collaboration, and more robust interdisciplinary dissemination to establish STD as a robust framework for the analysis of multidimensional data.