Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
2.4
Journals
Information
Special Issues
Information Visualization Theory and Applications
share announcement

Information Visualization Theory and Applications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 10646

Special Issue Editors

Dr. Dong Hyun Jeong

E-Mail Website
Guest Editor
Department of Computer Science and Information Technology, University of the District of Columbia, Washington, DC 20759, USA
Interests: HCI; visual analytics; big data analytics; information visualization
Dr. Soo-Yeon Ji

E-Mail Website
Guest Editor
Department of Computer Science, Bowie State University, Bowie, MD 20715, USA
Interests: data science; medical informatics and visualization; time-series analysis
Dr. Bong-Keun Jeong

E-Mail Website
Guest Editor
Department of Management and Decision Sciences, Coastal Carolina University, Conway, SC 29528, USA
Interests: data analytics; visualization; management information systems; HCI

Special Issue Information

Dear Colleagues,

In data science, information visualization has become a central component to understanding complex scientific problems or data through visualization. Thus, designing innovative visualization systems is essential to address and solve various domain problems. For this, a theoretical understanding of visualization models and design principles through visual encoding, interaction, and/or analysis tasks is critical to solving domain problems and understanding data effectively. Identifying possible implications from theories of perception, cognition, design, and/or aesthetics is also important. Automated design guidelines and visualization recommendations should be determined to find scientific limitations of understanding data through visualization.

This Special Issue aims to seek high-quality papers that highlight visualization challenges to be accomplished, elucidate solutions for understanding domain problems through visualizations, and theoretical visualization principles for advancing visualization techniques and applications.

Dr. Dong Hyun Jeong
Dr. Soo-Yeon Ji
Dr. Bong-Keun Jeong
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. Information 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

  • information visualization
  • science of user interactions in visualization
  • visualization theory
  • knowledge-assisted visualization
  • visualization technique
  • visualization in machine learning
  • visualziatioin applications and design studies
  • evaluation and empirical research in visualization
  • visual data analysis and knowledge discovery
  • visual representation and interaction
  • visualization applications
  • visualization taxonomies and models
  • visualization algorithms and technologies
  • uncertainty visualization
  • visualization tools and systems for simulation and modeling

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 policies 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

30 pages, 75307 KiB  
Article
Bridging the Gap Between Theory and Practice: Fitness Landscape Analysis of Real-World Problems with Nearest-Better Network
by Yiya Diao, Changhe Li, Junchen Wang, Sanyou Zeng and Shengxiang Yang
Information 2025, 16(3), 190; https://doi.org/10.3390/info16030190 - 1 Mar 2025
Viewed by 555
Abstract
For a long time, there has been a gap between theoretical optimization research and real-world applications. A key challenge is that many real-world problems are black-box problems, making it difficult to identify their characteristics and, consequently, select the most effective algorithms to solve [...] Read more.
For a long time, there has been a gap between theoretical optimization research and real-world applications. A key challenge is that many real-world problems are black-box problems, making it difficult to identify their characteristics and, consequently, select the most effective algorithms to solve them. Fortunately, the Nearest-Better Network has emerged as an effective tool for analyzing the characteristics of problems, regardless of dimensionality. In this paper, we conduct an in-depth experimental analysis of real-world functions from the CEC 2022 and CEC 2011 competitions using the NBN. Our experiments reveal that real-world problems often exhibit characteristics such as unclear global structure, multiple attraction basins, vast neutral regions around the global optimum, and high levels of ill conditioning. Full article
(This article belongs to the Special Issue Information Visualization Theory and Applications)
►▼ Show Figures

Figure 1

22 pages, 3983 KiB  
Article
Leveraging Machine Learning to Analyze Semantic User Interactions in Visual Analytics
by Dong Hyun Jeong, Bong Keun Jeong and Soo Yeon Ji
Information 2024, 15(6), 351; https://doi.org/10.3390/info15060351 - 13 Jun 2024
Viewed by 1346
Abstract
In the field of visualization, understanding users’ analytical reasoning is important for evaluating the effectiveness of visualization applications. Several studies have been conducted to capture and analyze user interactions to comprehend this reasoning process. However, few have successfully linked these interactions to users’ [...] Read more.
In the field of visualization, understanding users’ analytical reasoning is important for evaluating the effectiveness of visualization applications. Several studies have been conducted to capture and analyze user interactions to comprehend this reasoning process. However, few have successfully linked these interactions to users’ reasoning processes. This paper introduces an approach that addresses the limitation by correlating semantic user interactions with analysis decisions using an interactive wire transaction analysis system and a visual state transition matrix, both designed as visual analytics applications. The system enables interactive analysis for evaluating financial fraud in wire transactions. It also allows mapping captured user interactions and analytical decisions back onto the visualization to reveal their decision differences. The visual state transition matrix further aids in understanding users’ analytical flows, revealing their decision-making processes. Classification machine learning algorithms are applied to evaluate the effectiveness of our approach in understanding users’ analytical reasoning process by connecting the captured semantic user interactions to their decisions (i.e., suspicious, not suspicious, and inconclusive) on wire transactions. With the algorithms, an average of 72% accuracy is determined to classify the semantic user interactions. For classifying individual decisions, the average accuracy is 70%. Notably, the accuracy for classifying ‘inconclusive’ decisions is 83%. Overall, the proposed approach improves the understanding of users’ analytical decisions and provides a robust method for evaluating user interactions in visualization tools. Full article
(This article belongs to the Special Issue Information Visualization Theory and Applications)
►▼ Show Figures

Figure 1

22 pages, 9658 KiB  
Article
Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender System
by Mouadh Guesmi, Mohamed Amine Chatti, Shoeb Joarder, Qurat Ul Ain, Clara Siepmann, Hoda Ghanbarzadeh and Rawaa Alatrash
Information 2023, 14(7), 401; https://doi.org/10.3390/info14070401 - 14 Jul 2023
Cited by 7 | Viewed by 3163
Abstract
Significant attention has been paid to enhancing recommender systems (RS) with explanation facilities to help users make informed decisions and increase trust in and satisfaction with an RS. Justification and transparency represent two crucial goals in explainable recommendations. Different from transparency, which faithfully [...] Read more.
Significant attention has been paid to enhancing recommender systems (RS) with explanation facilities to help users make informed decisions and increase trust in and satisfaction with an RS. Justification and transparency represent two crucial goals in explainable recommendations. Different from transparency, which faithfully exposes the reasoning behind the recommendation mechanism, justification conveys a conceptual model that may differ from that of the underlying algorithm. An explanation is an answer to a question. In explainable recommendation, a user would want to ask questions (referred to as intelligibility types) to understand the results given by an RS. In this paper, we identify relationships between Why and How explanation intelligibility types and the explanation goals of justification and transparency. We followed the Human-Centered Design (HCD) approach and leveraged the What–Why–How visualization framework to systematically design and implement Why and How visual explanations in the transparent Recommendation and Interest Modeling Application (RIMA). Furthermore, we conducted a qualitative user study (N = 12) based on a thematic analysis of think-aloud sessions and semi-structured interviews with students and researchers to investigate the potential effects of providing Why and How explanations together in an explainable RS on users’ perceptions regarding transparency, trust, and satisfaction. Our study shows qualitative evidence confirming that the choice of the explanation intelligibility types depends on the explanation goal and user type. Full article
(This article belongs to the Special Issue Information Visualization Theory and Applications)
►▼ Show Figures

Figure 1

17 pages, 12932 KiB  
Article
On Isotropy of Multimodal Embeddings
by Kirill Tyshchuk, Polina Karpikova, Andrew Spiridonov, Anastasiia Prutianova, Anton Razzhigaev and Alexander Panchenko
Information 2023, 14(7), 392; https://doi.org/10.3390/info14070392 - 10 Jul 2023
Cited by 4 | Viewed by 4087
Abstract
Embeddings, i.e., vector representations of objects, such as texts, images, or graphs, play a key role in deep learning methodologies nowadays. Prior research has shown the importance of analyzing the isotropy of textual embeddings for transformer-based text encoders, such as the BERT model. [...] Read more.
Embeddings, i.e., vector representations of objects, such as texts, images, or graphs, play a key role in deep learning methodologies nowadays. Prior research has shown the importance of analyzing the isotropy of textual embeddings for transformer-based text encoders, such as the BERT model. Anisotropic word embeddings do not use the entire space, instead concentrating on a narrow cone in such a pretrained vector space, negatively affecting the performance of applications, such as textual semantic similarity. Transforming a vector space to optimize isotropy has been shown to be beneficial for improving performance in text processing tasks. This paper is the first comprehensive investigation of the distribution of multimodal embeddings using the example of OpenAI’s CLIP pretrained model. We aimed to deepen the understanding of the embedding space of multimodal embeddings, which has previously been unexplored in this respect, and study the impact on various end tasks. Our initial efforts were focused on measuring the alignment of image and text embedding distributions, with an emphasis on their isotropic properties. In addition, we evaluated several gradient-free approaches to enhance these properties, establishing their efficiency in improving the isotropy/alignment of the embeddings and, in certain cases, the zero-shot classification accuracy. Significantly, our analysis revealed that both CLIP and BERT models yielded embeddings situated within a cone immediately after initialization and preceding training. However, they were mostly isotropic in the local sense. We further extended our investigation to the structure of multilingual CLIP text embeddings, confirming that the observed characteristics were language-independent. By computing the few-shot classification accuracy and point-cloud metrics, we provide evidence of a strong correlation among multilingual embeddings. Embeddings transformation using the methods described in this article makes it easier to visualize embeddings. At the same time, multiple experiments that we conducted showed that, in regard to the transformed embeddings, the downstream tasks performance does not drop substantially (and sometimes is even improved). This means that one could obtain an easily visualizable embedding space, without substantially losing the quality of downstream tasks. Full article
(This article belongs to the Special Issue Information Visualization Theory and Applications)
►▼ 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