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Hybrid visualizations combine different metaphors into a single network layout, in order to help humans in finding the "right way" of displaying the different portions of the network, especially when it is globally sparse and locally dense. We investigate hybrid visualizations in two complementary directions: (i) On the one hand, we evaluate the effectiveness of different hybrid visualization models through a comparative user study; (ii) On the other hand, we estimate the usefulness of an interactive visualization that integrates all the considered hybrid models together. The results of our study provide some hints about the usefulness of the different hybrid visualizations for specific tasks of analysis and indicates that integrating different hybrid models into a single visualization may offer a valuable tool of analysis.

This paper explores the ageing population in Italy, where older adults account for more than 14 million individuals (in January 2023) and constitute 24.1 % of the total population. Frailty, a condition encompassing biological, psychological, social, and economic challenges, is recognised as a significant public health issue. The study introduces the Short Functional Geriatric Evaluation (SFGE) as a large-scale screening tool for frailty in community-dwelling older individuals. A Confirmatory Factor Analysis (CFA) was conducted on the SFGE. The CFA scrutinises the construct validity of SFGE using a sample population from the "Long Live the Elderly!" program in Italy. Initial results indicate an acceptable fit, prompting the incorporation of Modification Indices to enhance model performance. The refined CFA demonstrates that the SFGE model effectively captures the multidimensional nature of frailty. The text underscores the timeliness of identifying frailty, emphasising the need for simple, fast, and predictive tools to screen large populations efficiently., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)