Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns.
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http://dx.doi.org/10.1109/TVCG.2011.247 | DOI Listing |
BioData Min
January 2025
Department of Computer Science, Hanyang University, Seoul, Republic of Korea.
Background: Understanding the molecular properties of chemical compounds is essential for identifying potential candidates or ensuring safety in drug discovery. However, exploring the vast chemical space is time-consuming and costly, necessitating the development of time-efficient and cost-effective computational methods. Recent advances in deep learning approaches have offered deeper insights into molecular structures.
View Article and Find Full Text PDFSci Rep
January 2025
Inserm, Gustave Roussy, Centre for Research in Epidemiology and Population Health (CESP), "Exposome, Heredity, Cancer, and Health" Team, Université Paris-Saclay, UVSQ, 12 Avenue Paul Vaillant Couturier, 94805, Villejuif, France.
Persistent organic pollutants (POPs) are a group of organic chemical compounds. Contradictory results have emerged in epidemiological studies attempting to elucidate their relationship with breast cancer risk. This study explored the relationship between dietary exposures to multiple POPs and ER-positive breast cancer risk in the French E3N cohort study, using three different approaches to handle multicollinearity among exposures.
View Article and Find Full Text PDFCurr Res Neurobiol
June 2025
Department of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Germany.
Although the pathophysiology of pain has been investigated tremendously, there are still many open questions with regard to specific pain entities and their pain-related symptoms. To increase the translational impact of (preclinical) animal neuroimaging pain studies, the use of disease-specific pain models, as well as relevant stimulus modalities, are critical. We developed a comprehensive framework for brain network analysis combining functional magnetic resonance imaging (MRI) with graph-theory (GT) and data classification by linear discriminant analysis.
View Article and Find Full Text PDFJ Mol Graph Model
January 2025
Department of Chemistry Education, Farhangian University, P.O. Box 14665-889, Tehran, Iran. Electronic address:
In this study, the need for efficient detection of volatile organic compounds (VOCs) in environmental monitoring, industrial safety, is addressed by investigating borophene-based B36 nanoclusters as gas sensors. Density functional theory (DFT) calculations were employed to examine the adsorption behavior of ethanol, isobutanol, and acetone on B surfaces, with a focus on vibrational modes, reactivity, and adsorption energies. It was found that acetone exhibits the strongest interaction with pristine B, indicating its potential for robust sensing applications.
View Article and Find Full Text PDFAquat Toxicol
January 2025
School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China; Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, China. Electronic address:
As compound concentrations in aquatic environments increase, the habitat degradation of aquatic organisms underscores the growing importance of studying the impact of chemicals on diverse aquatic populations. Understanding the potential impacts of different chemical substances on different species is a necessary requirement for protecting the environment and ensuring sustainable human development. In this regard, deep learning methods offer significant advantages over traditional experimental approaches in terms of cost, accuracy, and generalization ability.
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