Vispubdata.org: A Metadata Collection About IEEE Visualization (VIS) Publications

Target Netgrams: An Annulus-Constrained Stress Model for Radial Graph Visualization

We present Target Netgrams as a visualization technique for radial layouts of graphs. Inspired by manually created target sociograms, we propose an annulus-constrained stress model that aims to position nodes onto the annuli between adjacent circles for indicating their radial hierarchy, while maintaining the network structure (clusters and neighborhoods) and improving readability as much as possible. This is achieved by having more space on the annuli than traditional layout techniques. By adapting stress majorization to this model, the layout is computed as a constrained least square optimization problem. Additional constraints (e.g., parent-child preservation, attribute-based clusters and structure-aware radii) are provided for exploring nodes, edges, and levels of interest. We demonstrate the effectiveness of our method through a comprehensive evaluation, a user study, and a case study.

Thirty-Two Years of IEEE VIS: Authors, Fields of Study and Citations

The IEEE VIS Conference (VIS) recently rebranded itself as a unified conference and officially positioned itself within the discipline of Data Science. Driven by this movement, we investigated (1) who contributed to VIS, and (2) where VIS stands in the scientific world. We examined the authors and fields of study of 3,240 VIS publications in the past 32 years based on data collected from OpenAlex and IEEE Xplore, among other sources. We also examined the citation flows from referenced papers (i.e., those referenced in VIS) to VIS, and from VIS to citing papers (i.e., those citing VIS). We found that VIS has been becoming increasingly popular and collaborative. The number of publications, of unique authors, and of participating countries have been steadily growing. Both cross-country collaborations, and collaborations between educational and non-educational affiliations, namely "cross-type collaborations", are increasing. The dominance of the US is decreasing, and authors from China are now an important part of VIS. In terms of author affiliation types, VIS is increasingly dominated by authors from universities. We found that the topics, inspirations, and influences of VIS research is limited such that (1) VIS, and their referenced and citing papers largely fall into the Computer Science domain, and (2) citations flow mostly between the same set of subfields within Computer Science. Our citation analyses showed that award-winning VIS papers had higher citations. Interactive visualizations, replication data, source code and supplementary material are available at https://32vis.hongtaoh.com and https://osf.io/zkvjm.

In Defence of Visual Analytics Systems: Replies to Critics

The last decade has witnessed many visual analytics (VA) systems that make successful applications to wide-ranging domains like urban analytics and explainable AI. However, their research rigor and contributions have been extensively challenged within the visualization community. We come in defence of VA systems by contributing two interview studies for gathering critics and responses to those criticisms. First, we interview 24 researchers to collect criticisms the review comments on their VA work. Through an iterative coding and refinement process, the interview feedback is summarized into a list of 36 common criticisms. Second, we interview 17 researchers to validate our list and collect their responses, thereby discussing implications for defending and improving the scientific values and rigor of VA systems. We highlight that the presented knowledge is deep, extensive, but also imperfect, provocative, and controversial, and thus recommend reading with an inclusive and critical eye. We hope our work can provide thoughts and foundations for conducting VA research and spark discussions to promote the research field forward more rigorously and vibrantly.

Multiscale Visualization: A Structured Literature Analysis

Multiscale visualizations are typically used to analyze multiscale processes and data in various application domains, such as the visual exploration of hierarchical genome structures in molecular biology. However, creating such multiscale visualizations remains challenging due to the plethora of existing work and the expression ambiguity in visualization research. Up to today, there has been little work to compare and categorize multiscale visualizations to understand their design practices. In this article, we present a structured literature analysis to provide an overview of common design practices in multiscale visualization research. We systematically reviewed and categorized 122 published journal or conference articles between 1995 and 2020. We organized the reviewed articles in a taxonomy that reveals common design factors. Researchers and practitioners can use our taxonomy to explore existing work to create new multiscale navigation and visualization techniques. Based on the reviewed articles, we examine research trends and highlight open research challenges.

Scalable Scalable Vector Graphics: Automatic Translation of Interactive SVGs to a Multithread VDOM for Fast Rendering

The dominant markup language for Web visualizations-Scalable Vector Graphics (SVG)-is comparatively easy to learn, and is open, accessible, customizable via CSS, and searchable via the DOM, with easy interaction handling and debugging. Because these attributes allow visualization creators to focus on design on implementation details, tools built on top of SVG, such as D3.js, are essential to the visualization community. However, slow SVG rendering can limit designs by effectively capping the number of on-screen data points, and this can force visualization creators to switch to Canvas or WebGL. These are less flexible (e.g., no search or styling via CSS), and harder to learn. We introduce Scalable Scalable Vector Graphics (SSVG) to reduce these limitations and allow complex and smooth visualizations to be created with SVG. SSVG automatically translates interactive SVG visualizations into a dynamic virtual DOM (VDOM) to bypass the browser's slow 'to specification' rendering by intercepting JavaScript function calls. De-coupling the SVG visualization specification from SVG rendering, and obtaining a dynamic VDOM, creates flexibility and opportunity for visualization system research. SSVG uses this flexibility to free up the main thread for more interactivity and renders the visualization with Canvas or WebGL on a web worker. Together, these concepts create a drop-in JavaScript library which can improve rendering performance by 3-9× with only one line of code added. To demonstrate applicability, we describe the use of SSVG on multiple example visualizations including published visualization research. A free copy of this article, collected data, and source code are available as open science at osf.io/ge8wp.

Artificial Intelligence-Based Medical Data Mining

Understanding published unstructured textual data using traditional text mining approaches and tools is becoming a challenging issue due to the rapid increase in electronic open-source publications. The application of data mining techniques in the medical sciences is an emerging trend; however, traditional text-mining approaches are insufficient to cope with the current upsurge in the volume of published data. Therefore, artificial intelligence-based text mining tools are being developed and used to process large volumes of data and to explore the hidden features and correlations in the data. This review provides a clear-cut and insightful understanding of how artificial intelligence-based data-mining technology is being used to analyze medical data. We also describe a standard process of data mining based on CRISP-DM (Cross-Industry Standard Process for Data Mining) and the most common tools/libraries available for each step of medical data mining.

embComp: Visual Interactive Comparison of Vector Embeddings

This article introduces embComp, a novel approach for comparing two embeddings that capture the similarity between objects, such as word and document embeddings. We survey scenarios where comparing these embedding spaces is useful. From those scenarios, we derive common tasks, introduce visual analysis methods that support these tasks, and combine them into a comprehensive system. One of embComp's central features are overview visualizations that are based on metrics for measuring differences in the local structure around objects. Summarizing these local metrics over the embeddings provides global overviews of similarities and differences. Detail views allow comparison of the local structure around selected objects and relating this local information to the global views. Integrating and connecting all of these components, embComp supports a range of analysis workflows that help understand similarities and differences between embedding spaces. We assess our approach by applying it in several use cases, including understanding corpora differences via word vector embeddings, and understanding algorithmic differences in generating embeddings.

VITALITY: Promoting Serendipitous Discovery of Academic Literature with Transformers & Visual Analytics

There are a few prominent practices for conducting reviews of academic literature, including searching for specific keywords on Google Scholar or checking citations from some initial seed paper(s). These approaches serve a critical purpose for academic literature reviews, yet there remain challenges in identifying relevant literature when similar work may utilize different terminology (e.g., mixed-initiative visual analytics papers may not use the same terminology as papers on model-steering, yet the two topics are relevant to one another). In this paper, we introduce a system, VITALITY, intended to complement existing practices. In particular, VITALITY promotes serendipitous discovery of relevant literature using transformer language models, allowing users to find semantically similar papers in a word embedding space given (1) a list of input paper(s) or (2) a working abstract. VITALITY visualizes this document-level embedding space in an interactive 2-D scatterplot using dimension reduction. VITALITY also summarizes meta information about the document corpus or search query, including keywords and co-authors, and allows users to save and export papers for use in a literature review. We present qualitative findings from an evaluation of VITALITY, suggesting it can be a promising complementary technique for conducting academic literature reviews. Furthermore, we contribute data from 38 popular data visualization publication venues in VITALITY, and we provide scrapers for the open-source community to continue to grow the list of supported venues.

Gender in 30 Years of IEEE Visualization

We present an exploratory analysis of gender representation among the authors, committee members, and award winners at the IEEE Visualization (VIS) conference over the last 30 years. Our goal is to provide descriptive data on which diversity discussions and efforts in the community can build. We look in particular at the gender of VIS authors as a proxy for the community at large. We consider measures of overall gender representation among authors, differences in careers, positions in author lists, and collaborations. We found that the proportion of female authors has increased from 9% in the first five years to 22% in the last five years of the conference. Over the years, we found the same representation of women in program committees and slightly more women in organizing committees. Women are less likely to appear in the last author position, but more in the middle positions. In terms of collaboration patterns, female authors tend to collaborate more than expected with other women in the community. All non-gender related data is available on https://osf.io/ydfj4/ and the gender-author matching can be accessed through https://nyu.databrary.org/volume/1301.

Simultaneous Matrix Orderings for Graph Collections

Undirected graphs are frequently used to model phenomena that deal with interacting objects, such as social networks, brain activity and communication networks. The topology of an undirected graph G can be captured by an adjacency matrix; this matrix in turn can be visualized directly to give insight into the graph structure. Which visual patterns appear in such a matrix visualization crucially depends on the ordering of its rows and columns. Formally defining the quality of an ordering and then automatically computing a high-quality ordering are both challenging problems; however, effective heuristics exist and are used in practice. Often, graphs do not exist in isolation but as part of a collection of graphs on the same set of vertices, for example, brain scans over time or of different people. To visualize such graph collections, we need a single ordering that works well for all matrices simultaneously. The current state-of-the-art solves this problem by taking a (weighted) union over all graphs and applying existing heuristics. However, this union leads to a loss of information, specifically in those parts of the graphs which are different. We propose a collection-aware approach to avoid this loss of information and apply it to two popular heuristic methods: leaf order and barycenter.The de-facto standard computational quality metrics for matrix ordering capture only block-diagonal patterns (cliques). Instead, we propose to use Moran's I, a spatial auto-correlation metric, which captures the full range of established patterns. Moran's I refines previously proposed stress measures. Furthermore, the popular leaf order method heuristically optimizes a similar measure which further supports the use of Moran's I in this context. An ordering that maximizes Moran's I can be computed via solutions to the Traveling Salesperson Problem (TSP); orderings that approximate the optimal ordering can be computed more efficiently, using any of the approximation algorithms for metric TSP. We evaluated our methods for simultaneous orderings on real-world datasets using Moran's I as the quality metric. Our results show that our collection-aware approach matches or improves performance compared to the union approach, depending on the similarity of the graphs in the collection. Specifically, our Moran's I-based collection-aware leaf order implementation consistently outperforms other implementations. Our collection-aware implementations carry no significant additional computational costs.

Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers

How to achieve academic career success has been a long-standing research question in social science research. With the growing availability of large-scale well-documented academic profiles and career trajectories, scholarly interest in career success has been reinvigorated, which has emerged to be an active research domain called the Science of Science (i.e., SciSci). In this study, we adopt an innovative dynamic perspective to examine how individual and social factors will influence career success over time. We propose ACSeeker, an interactive visual analytics approach to explore the potential factors of success and how the influence of multiple factors changes at different stages of academic careers. We first applied a Multi-factor Impact Analysis framework to estimate the effect of different factors on academic career success over time. We then developed a visual analytics system to understand the dynamic effects interactively. A novel timeline is designed to reveal and compare the factor impacts based on the whole population. A customized career line showing the individual career development is provided to allow a detailed inspection. To validate the effectiveness and usability of ACSeeker, we report two case studies and interviews with a social scientist and general researchers.

VIS30K: A Collection of Figures and Tables From IEEE Visualization Conference Publications

We present the VIS30K dataset, a collection of 29,689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST). VIS30K's comprehensive coverage of the scientific literature in visualization not only reflects the progress of the field but also enables researchers to study the evolution of the state-of-the-art and to find relevant work based on graphical content. We describe the dataset and our semi-automatic collection process, which couples convolutional neural networks (CNN) with curation. Extracting figures and tables semi-automatically allows us to verify that no images are overlooked or extracted erroneously. To improve quality further, we engaged in a peer-search process for high-quality figures from early IEEE Visualization papers. With the resulting data, we also contribute VISImageNavigator (VIN, visimagenavigator.github.io), a web-based tool that facilitates searching and exploring VIS30K by author names, paper keywords, title and abstract, and years.

Integrating Prior Knowledge in Mixed-Initiative Social Network Clustering

We propose a new approach-called PK-clustering-to help social scientists create meaningful clusters in social networks. Many clustering algorithms exist but most social scientists find them difficult to understand, and tools do not provide any guidance to choose algorithms, or to evaluate results taking into account the prior knowledge of the scientists. Our work introduces a new clustering approach and a visual analytics user interface that address this issue. It is based on a process that 1) captures the prior knowledge of the scientists as a set of incomplete clusters, 2) runs multiple clustering algorithms (similarly to clustering ensemble methods), 3) visualizes the results of all the algorithms ranked and summarized by how well each algorithm matches the prior knowledge, 4) evaluates the consensus between user-selected algorithms and 5) allows users to review details and iteratively update the acquired knowledge. We describe our approach using an initial functional prototype, then provide two examples of use and early feedback from social scientists. We believe our clustering approach offers a novel constructive method to iteratively build knowledge while avoiding being overly influenced by the results of often randomly selected black-box clustering algorithms.

Composition and Configuration Patterns in Multiple-View Visualizations

Multiple-view visualization (MV) is a layout design technique often employed to help users see a large number of data attributes and values in a single cohesive representation. Because of its generalizability, the MV design has been widely adopted by the visualization community to help users examine and interact with large, complex, and high-dimensional data. However, although ubiquitous, there has been little work to categorize and analyze MVs in order to better understand its design space. As a result, there has been little to no guideline in how to use the MV design effectively. In this paper, we present an in-depth study of how MVs are designed in practice. We focus on two fundamental measures of multiple-view patterns: composition, which quantifies what view types and how many are there; and configuration, which characterizes spatial arrangement of view layouts in the display space. We build a new dataset containing 360 images of MVs collected from IEEE VIS, EuroVis, and PacificVis publications 2011 to 2019, and make fine-grained annotations of view types and layouts for these visualization images. From this data we conduct composition and configuration analyses using quantitative metrics of term frequency and layout topology. We identify common practices around MVs, including relationship of view types, popular view layouts, and correlation between view types and layouts. We combine the findings into a MV recommendation system, providing interactive tools to explore the design space, and support example-based design.

Defragmenting Research Areas with Knowledge Visualization and Visual Text Analytics

The increasing specialization of science is motivating the fragmentation of traditional and well-established research areas into interdisciplinary communities of practice that focus on cooperation between experts to solve problems in a wide range of domains. This is the case of problem-driven visualization research (PDVR), in which groups of scholars use visualization techniques in different application domains such as the digital humanities, bioinformatics, sports science, or computer security. In this paper, we employ the findings obtained during the development of a novel visual text analytics tool we built in previous studies, GlassViz, to automatically detect interesting knowledge associations and groups of common interests between these communities of practice. Our proposed method relies on the statistical modeling of author-assigned keywords to make its findings, which are demonstrated in two use cases. The results show that it is possible to propose interactive, semisupervised visual approaches that aim at defragmenting a body of research using text-based, automatic literature analysis methods.

VIStory: interactive storyboard for exploring visual information in scientific publications

Abstract

Many visual analytics have been developed for examining scientific publications comprising wealthy data such as authors and citations. The studies provide unprecedented insights on a variety of applications, e.g., literature review and collaboration analysis. However, visual information (e.g., figures) that is widely employed for storytelling and methods description are often neglected. We present VIStory, an interactive storyboard for exploring visual information in scientific publications. We harvest a new dataset of a large corpora of figures, using an automatic figure extraction method. Each figure contains various attributes such as dominant color and width/height ratio, together with faceted metadata of the publication including venues, authors, and keywords. To depict these information, we develop an intuitive interface consisting of three components: (1) Faceted View enables efficient query by publication metadata, benefiting from a nested table structure, (2) Storyboard View arranges paper rings—a well-designed glyph for depicting figure attributes, in a themeriver layout to reveal temporal trends, and (3) Endgame View presents a highlighted figure together with the publication metadata. We illustrate the applicability of VIStory with case studies on two datasets, i.e., 10-year IEEE VIS publications, and publications by a research team at CVPR, ICCV, and ECCV conferences. Quantitative and qualitative results from a formal user study demonstrate the efficiency of VIStory in exploring visual information in scientific publications.

Graphical abstract

Electronic supplementary material

The online version of this article (10.1007/s12650-020-00688-1) contains supplementary material, which is available to authorized users.

A Data-Driven Introduction to Authors, Readings, and Techniques in Visualization for the Digital Humanities

The newly rediscovered frontier between data visualization and the digital humanities has proven to be an exciting field of experimentation for scholars from both disciplines. This fruitful collaboration is attracting researchers from other areas of science who may be willing to create visual analysis tools that promote humanities research in its many forms. However, as the collaboration grows in complexity, it may become intimidating for these scholars to get engaged in the discipline. To facilitate this task, we have built an introduction to visualization for the digital humanities that sits on a data-driven stance adopted by the authors. In order to construct a dataset representative of the discipline, we analyze citations from a core corpus on 300 publications in visualization for the humanities obtained from recent editions of the InfoVis Vis4DH workshop, the ADHO Digital Humanities Conference, and the specialized digital humanities journal Digital Humanities Quarterly. From here, we extract referenced works and analyze more than 1900 publications in search of citation patterns, prominent authors in the field, and other interesting insights. Finally, following the path set by other researchers in the visualization and Human-Computer Interaction (HCI) communities, we analyze paper keywords to identify significant themes and research opportunities in the field.

Galex: Exploring the Evolution and Intersection of Disciplines

Revealing the evolution of science and the intersections among its sub-fields is extremely important to understand the characteristics of disciplines, discover new topics, and predict the future. The current work focuses on either building the skeleton of science, lacking interaction, detailed exploration and interpretation or on the lower topic level, missing high-level macro-perspective. To fill this gap, we design and implement Galaxy Evolution Explorer (Galex), a hierarchical visual analysis system, in combination with advanced text mining technologies, that could help analysts to comprehend the evolution and intersection of one discipline rapidly. We divide Galex into three progressively fine-grained levels: discipline, area, and institution levels. The combination of interactions enables analysts to explore an arbitrary piece of history and an arbitrary part of the knowledge space of one discipline. Using a flexible spotlight component, analysts could freely select and quickly understand an exploration region. A tree metaphor allows analysts to perceive the expansion, decline, and intersection of topics intuitively. A synchronous spotlight interaction aids in comparing research contents among institutions easily. Three cases demonstrate the effectiveness of our system.

Bridging Text Visualization and Mining: A Task-Driven Survey

Visual text analytics has recently emerged as one of the most prominent topics in both academic research and the commercial world. To provide an overview of the relevant techniques and analysis tasks, as well as the relationships between them, we comprehensively analyzed 263 visualization papers and 4,346 mining papers published between 1992-2017 in two fields: visualization and text mining. From the analysis, we derived around 300 concepts (visualization techniques, mining techniques, and analysis tasks) and built a taxonomy for each type of concept. The co-occurrence relationships between the concepts were also extracted. Our research can be used as a stepping-stone for other researchers to 1) understand a common set of concepts used in this research topic; 2) facilitate the exploration of the relationships between visualization techniques, mining techniques, and analysis tasks; 3) understand the current practice in developing visual text analytics tools; 4) seek potential research opportunities by narrowing the gulf between visualization and mining techniques based on the analysis tasks; and 5) analyze other interdisciplinary research areas in a similar way. We have also contributed a web-based visualization tool for analyzing and understanding research trends and opportunities in visual text analytics.

Visual Analysis of Research Paper Collections Using Normalized Relative Compression

The analysis of research paper collections is an interesting topic that can give insights on whether a research area is stalled in the same problems, or there is a great amount of novelty every year. Previous research has addressed similar tasks by the analysis of keywords or reference lists, with different degrees of human intervention. In this paper, we demonstrate how, with the use of Normalized Relative Compression, together with a set of automated data-processing tasks, we can successfully visually compare research articles and document collections. We also achieve very similar results with Normalized Conditional Compression that can be applied with a regular compressor. With our approach, we can group papers of different disciplines, analyze how a conference evolves throughout the different editions, or how the profile of a researcher changes through the time. We provide a set of tests that validate our technique, and show that it behaves better for these tasks than other techniques previously proposed.

Juniper: A Tree+ Table Approach to Multivariate Graph Visualization

Analyzing large, multivariate graphs is an important problem in many domains, yet such graphs are challenging to visualize. In this paper, we introduce a novel, scalable, tree+table multivariate graph visualization technique, which makes many tasks related to multivariate graph analysis easier to achieve. The core principle we follow is to selectively query for nodes or subgraphs of interest and visualize these subgraphs as a spanning tree of the graph. The tree is laid out linearly, which enables us to juxtapose the nodes with a table visualization where diverse attributes can be shown. We also use this table as an adjacency matrix, so that the resulting technique is a hybrid node-link/adjacency matrix technique. We implement this concept in Juniper and complement it with a set of interaction techniques that enable analysts to dynamically grow, restructure, and aggregate the tree, as well as change the layout or show paths between nodes. We demonstrate the utility of our tool in usage scenarios for different multivariate networks: a bipartite network of scholars, papers, and citation metrics and a multitype network of story characters, places, books, etc.

Keshif: Rapid and Expressive Tabular Data Exploration for Novices

General purpose graphical interfaces for data exploration are typically based on manual visualization and interaction specifications. While designing manual specification can be very expressive, it demands high efforts to make effective decisions, therefore reducing exploratory speed. Instead, principled automated designs can increase exploratory speed, decrease learning efforts, help avoid ineffective decisions, and therefore better support data analytics novices. Towards these goals, we present Keshif, a new systematic design for tabular data exploration. To summarize a given dataset, Keshif aggregates records by value within attribute summaries, and visualizes aggregate characteristics using a consistent design based on data types. To reveal data distribution details, Keshif features three complementary linked selections: highlighting, filtering, and comparison. Keshif further increases expressiveness through aggregate metrics, absolute/part-of scale modes, calculated attributes, and saved selections, all working in synchrony. Its automated design approach also simplifies authoring of dashboards composed of summaries and individual records from raw data using fluid interaction. We show examples selected from datasets from diverse domains. Our study with novices shows that after exploring raw data for 15 minutes, our participants reached close to 30 data insights on average, comparable to other studies with skilled users using more complex tools.