Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
1.8
Journals
Algorithms
Special Issues
Graph Drawing and Information Visualization
share announcement

Graph Drawing and Information Visualization

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (15 October 2020) | Viewed by 14926

Special Issue Editors

Prof. Dr. Alex Telea

E-Mail Website
Guest Editor
Department of Information and Computing Sciences, Faculty of Science, Utrecht University, 3584 CC Utrecht, The Netherlands
Interests: data and information visualization; visual data analytics for high-dimensional data and machine learning; visual analytics for software comprehension
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. David Auber

E-Mail Website
Guest Editor
Laboratoire Bordelais de Recherche en Informatique (LaBRI), University of Toulouse, 33405 Talence, France
Interests: information visualization; large graph visualization; simplified visualization; quality metrics for information visualization

Special Issue Information

Dear Colleagues,

In the past several decades, graph (or network) visualization and information visualization have massively evolved and led to many results, ranging from theoretical studies to algorithms, techniques, tools, and case studies. Graph visualization focuses on the drawing and interactive exploration of relational datasets, also called graphs or networks. Information visualization focuses on the larger goal of the depiction and visual exploration of non-spatial, abstract, hybrid, and multi-type data. Current problems in science and engineering generate large amounts of data that are multivariate, vary in time, and have both spatial and non-spatial attributes of different types. As such, while the graph drawing and information visualization communities have traditionally evolved along separate lines, there is an increasing need for researchers and practitioners to combine and share results in both areas.

The aim of this Special Issue is to further bridge the still-existing gap between the graph drawing and information visualization communities, by showing how results obtained in one of the communities can be used, adapted, or enhanced to address problems and use-cases typically emerging in the other. For this, we invite researchers and practitioners that work at the intersection of the two communities to submit their original and unpublished works to this Special Issue. Of particular interest are papers that describe techniques, methods, studies, and tools that combine interactive graph visualization and more general information visualization techniques to solve a given problem, following a visual analytics approach. Example of specific topics of interest are outlined below. However, other topics at the crossroads of graph and information visualization are of equal interest.

Prof. Dr. Alex Telea
Prof. Dr. David Auber
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Very large graph visualization
  • Visualization of multivariate and multi-attributed graphs
  • Interaction techniques and metaphors for graph visual exploration
  • User studies in graph visualization
  • Aesthetic and perceptual factors, criteria, and quality metrics in graph visualization
  • Novel visual metaphors for graph representation
  • Deep learning techniques for graph visualization
  • Graph visualization in visual analytics applications
  • Visualization and exploration of large dynamic graphs
  • Novel graph and network visualization interfaces
  • Visualization of (large) graphs and networks in real-world applications
  • Engineering of network visualization systems and tools

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

26 pages, 31259 KiB  
Article
Structure-Aware Trail Bundling for Large DTI Datasets
by Steven Bouma, Christophe Hurter and Alexandru Telea
Algorithms 2020, 13(12), 316; https://doi.org/10.3390/a13120316 - 30 Nov 2020
Viewed by 2247
Abstract
Creating simplified visualizations of large 3D trail sets with limited occlusion and preservation of the main structures in the data is challenging. We address this challenge for the specific context of 3D fiber trails created by DTI tractography. For this, we propose to [...] Read more.
Creating simplified visualizations of large 3D trail sets with limited occlusion and preservation of the main structures in the data is challenging. We address this challenge for the specific context of 3D fiber trails created by DTI tractography. For this, we propose to jointly simplify trails in both the geometric space (by extending and adapting an existing bundling method to handle 3D trails) and in the image space (by proposing several shading and rendering techniques). Our method can handle 3D datasets of hundreds of thousands of trails at interactive rate, has parameters for the most of which good preset values are given, and produces visualizations that have been found, in a small-scale user study involving five medical professionals, to be better in occlusion reduction, conveying the connectivity structure of the brain, and overall clarity than existing methods for the same data. We demonstrate our technique with several real-world public DTI datasets. Full article
(This article belongs to the Special Issue Graph Drawing and Information Visualization)
►▼ Show Figures

Figure 1

23 pages, 29018 KiB  
Article
Graphs from Features: Tree-Based Graph Layout for Feature Analysis
by Rosane Minghim, Liz Huancapaza, Erasmo Artur, Guilherme P. Telles and Ivar V. Belizario
Algorithms 2020, 13(11), 302; https://doi.org/10.3390/a13110302 - 18 Nov 2020
Cited by 2 | Viewed by 4812
Abstract
Feature Analysis has become a very critical task in data analysis and visualization. Graph structures are very flexible in terms of representation and may encode important information on features but are challenging in regards to layout being adequate for analysis tasks. In this [...] Read more.
Feature Analysis has become a very critical task in data analysis and visualization. Graph structures are very flexible in terms of representation and may encode important information on features but are challenging in regards to layout being adequate for analysis tasks. In this study, we propose and develop similarity-based graph layouts with the purpose of locating relevant patterns in sets of features, thus supporting feature analysis and selection. We apply a tree layout in the first step of the strategy, to accomplish node placement and overview based on feature similarity. By drawing the remainder of the graph edges on demand, further grouping and relationships among features are revealed. We evaluate those groups and relationships in terms of their effectiveness in exploring feature sets for data analysis. Correlation of features with a target categorical attribute and feature ranking are added to support the task. Multidimensional projections are employed to plot the dataset based on selected attributes to reveal the effectiveness of the feature set. Our results have shown that the tree-graph layout framework allows for a number of observations that are very important in user-centric feature selection, and not easy to observe by any other available tool. They provide a way of finding relevant and irrelevant features, spurious sets of noisy features, groups of similar features, and opposite features, all of which are essential tasks in different scenarios of data analysis. Case studies in application areas centered on documents, images and sound data demonstrate the ability of the framework to quickly reach a satisfactory compact representation from a larger feature set. Full article
(This article belongs to the Special Issue Graph Drawing and Information Visualization)
►▼ Show Figures

Graphical abstract

18 pages, 16856 KiB  
Article
Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility
by Ignacio Pérez-Messina, Eduardo Graells-Garrido, María Jesús Lobo and Christophe Hurter
Algorithms 2020, 13(11), 298; https://doi.org/10.3390/a13110298 - 15 Nov 2020
Cited by 4 | Viewed by 4087
Abstract
Pervasive data have become a key source of information for mobility and transportation analyses. However, as a secondary source, it has a different methodological origin than travel survey data, usually relying on unsupervised algorithms, and so it requires to be assessed as a [...] Read more.
Pervasive data have become a key source of information for mobility and transportation analyses. However, as a secondary source, it has a different methodological origin than travel survey data, usually relying on unsupervised algorithms, and so it requires to be assessed as a dataset. This assessment is challenging, because, in general, there is not a benchmark dataset or a ground truth scenario available, as travel surveys only represent a partial view of the phenomenon and suffer from their own biases. For this critical task, which involves urban planners and data scientists, we study the design space of the visualization of cross-origin, multivariate flow datasets. For this purpose, we introduce the Modalflow system, which incorporates and adapts different visualization techniques in a notebook-like setting, presenting novel visual encodings and interactions for flows with modal partition into scatterplots, flow maps, origin-destination matrices, and ternary plots. Using this system, we extract general insights on visual analysis of pervasive and survey data for urban mobility and assess a mobile phone network dataset for one metropolitan area. Full article
(This article belongs to the Special Issue Graph Drawing and Information Visualization)
►▼ Show Figures

Figure 1

27 pages, 55554 KiB  
Article
Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
by Fabio Sikansi, Renato R. O. da Silva, Gabriel D. Cantareira, Elham Etemad and Fernando V. Paulovich
Algorithms 2020, 13(11), 290; https://doi.org/10.3390/a13110290 - 10 Nov 2020
Cited by 1 | Viewed by 2924
Abstract
Graph visualization has been successfully applied in a wide range of problems and applications. Although different approaches are available to create visual representations, most of them suffer from clutter when faced with many nodes and/or edges. Among the techniques that address this problem, [...] Read more.
Graph visualization has been successfully applied in a wide range of problems and applications. Although different approaches are available to create visual representations, most of them suffer from clutter when faced with many nodes and/or edges. Among the techniques that address this problem, edge bundling has attained relative success in improving node-link layouts by bending and aggregating edges. Despite their success, most approaches perform the bundling based only on visual space information. There is no explicit connection between the produced bundled visual representation and the underlying data (edges or vertices attributes). In this paper, we present a novel edge bundling technique, called Similarity-Driven Edge Bundling (SDEB), to address this issue. Our method creates a similarity hierarchy based on a multilevel partition of the data, grouping edges considering the similarity between nodes to guide the bundling. The novel features introduced by SDEB are explored in different application scenarios, from dynamic graph visualization to multilevel exploration. Our results attest that SDEB produces layouts that consistently follow the similarity relationships found in the graph data, resulting in semantically richer presentations that are less cluttered than the state-of-the-art. Full article
(This article belongs to the Special Issue Graph Drawing and Information Visualization)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop