Information entropy metrics have been applied to a wide range of problems that were abstracted as complex networks. This growing body of research is scattered in multiple disciplines, which makes it difficult to identify available metrics and understand the context in which they are applicable. In this work, a narrative literature review of information entropy metrics for complex networks is conducted following the PRISMA guidelines. Existing entropy metrics are classified according to three different criteria: whether the metric provides a property of the graph or a graph component (such as the nodes), the chosen probability distribution, and the types of complex networks to which the metrics are applicable. Consequently, this work identifies the areas in need for further development aiming to guide future research efforts.
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http://dx.doi.org/10.3390/e22121417 | DOI Listing |
Sci Rep
January 2025
Department of Mathematical Sciences, Faculty of Science, Somali National University, Mogadishu Campus, Mogadishu, Somalia.
In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy.
View Article and Find Full Text PDFJ Clin Neurophysiol
January 2025
Department of Intensive Care, Neuro-Intensive Care Unit, University Hospital of Geneva, Geneva, Switzerland.
Purpose: Recent research on quantitative EEG in coma has proposed several metrics correlating with consciousness level. However, the heterogeneous nature of coma can challenge the generalizability of these measures. This study investigates alpha-coma, an electroclinical pattern characterized by a widespread, nonreactive alpha rhythm often linked to poor outcomes.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
Department of Nuclear Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730030 China.
Unlabelled: Juvenile myoclonic epilepsy (JME) exhibits abnormal functional connectivity of brain networks at multiple frequencies. We used the multilayer network model to address the heterogeneous features at different frequencies and assess the mechanisms of functional integration and segregation of brain networks in JME patients. To address the possibility of false edges or missing edges during network construction, we combined multilayer networks with link prediction techniques.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, 75660, Pakistan.
Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challenging to trust predictions, particularly in uncertain cases. This sort of uncertainty can be crucial in medical image analysis; diabetic retinopathy is an example where even slight errors without an indication of confidence can have adverse impacts.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Universidad Nacional de Córdoba - Facultad de Ciencias Agropecuarias, X5000HUA, Córdoba, Argentina.
Landscape metrics (LM) play a crucial role in fields such as urban planning, ecology, and environmental research, providing insights into the ecological and functional dynamics of ecosystems. However, in dynamic systems, generating thematic maps for LM analysis poses challenges due to the substantial data volume required and issues such as cloud cover interruptions. The aim of this study was to compare the accuracy of land cover maps produced by three temporal aggregation methods: median reflectance, maximum normalised difference vegetation index (NDVI), and a two-date image stack using Sentinel-2 (S2) and then to analyse their implications for LM calculation.
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