Research in ML4VIS investigates how to use machine learning (ML) techniques to generate visualizations, and the field is rapidly growing with high societal impact. However, as with any computational pipeline that employs ML processes, ML4VIS approaches are susceptible to a range of ML-specific adversarial attacks. These attacks can manipulate visualization generations, causing analysts to be tricked and their judgments to be impaired. Due to a lack of synthesis from both visualization and ML perspectives, this security aspect is largely overlooked by the current ML4VIS literature. To bridge this gap, we investigate the potential vulnerabilities of ML-aided visualizations from adversarial attacks using a holistic lens of both visualization and ML perspectives. We first identify the attack surface (i.e., attack entry points) that is unique in ML-aided visualizations. We then exemplify five different adversarial attacks. These examples highlight the range of possible attacks when considering the attack surface and multiple different adversary capabilities. Our results show that adversaries can induce various attacks, such as creating arbitrary and deceptive visualizations, by systematically identifying input attributes that are influential in ML inferences. Based on our observations of the attack surface characteristics and the attack examples, we underline the importance of comprehensive studies of security issues and defense mechanisms as a call of urgency for the ML4VIS community.

Process exploration is a critical task in process mining, enabling analysts to uncover insights and generate hypotheses about process behavior from event log data. Traditional approaches often rely onstatic, single-facet (control-flow) visualizations, such as Directly-Follows Graphs, that limit flexibility and obscure multi-perspective dependencies. In this paper, we introduce a novel visual analytics approach that leverages coordinated projections to support multi-faceted process exploration. Through dimensionality reduction (e.g., UMAP, t-SNE) and topic modeling, our method generates coordinated views that dynamically link different facets of process data, enabling cross-perspective exploration. Interaction techniques, such as brushing-and-linking and glyph-based representations, could further enhance the analyst’s ability to correlate patterns across dimensions. We demonstrate the effectiveness of our approach using a real-world traffic fines event log.

The pandemic had broad reaching impacts on how we do many things including the way that we design and implement visualizations. In this article, we reflect on how visualization design changed in an emergency response. Based on these reflections, we present modifications to design methodologies for visualizations to accommodate an emergency response and its working conditions.

Similarity-based analysis is a common and intuitive tool for exploring large data sets. For instance, grouping data items by their level of similarity, regarding one or several chosen aspects, can reveal patterns and relations from the intrinsic structure of the data and thus provide important insights in the sense-making process. Existing analytical methods (such as clustering and dimensionality reduction) tend to target questions such as "Which objects are similar?"; but since they are not necessarily well-suited to answer questions such as "How does the result change if we change the similarity criteria?" or "How are the items linked together by the similarity relations?" they do not unlock the full potential of similarity-based analysis—and here we see a gap to fill. In this paper, we propose that the concept of similarity could be regarded as both: (1) a relation between items, and (2) a property in its own, with a specific distribution over the data set. Based on this approach, we developed an embedding-based computational pipeline together with a prototype visual analytics tool which allows the user to perform similarity-based exploration of a large set of scientific publications. To demonstrate the potential of our method, we present two different use cases, and we also discuss the strengths and limitations of our approach.

This study presents an interactive visualization tool that facilitates fairness-aware urban planning. The system introduces afairness scale to assess the accessibility of potential new developments, using color-coded scatter plots to visualize disparities.An intuitive interaction design minimizes complexity while enhancing usability, enabling users to analyze urban infrastructureand services. Developed with web technologies, the tool leverages OpenStreetMap data to ensure adaptability across differentcities. Future optimizations include advanced analytical capabilities and broader dataset integrations to improve decision-making in urban development.

We are deeply saddened by the unexpected passing of Dr. Chris Weaver on the morning of Sunday, 29 June 2025. Chris was a valued and longtime member of the IEEE Computer Graphics and Applications (IEEE CG&A) editorial board and most recently served as Associate Editor-in-Chief of Special Issues.

An associate professor in the School of Computer Science at the University of Oklahoma, Chris earned a Ph.D. in Computer Science from the University of Wisconsin-Madison. From 2005 to 2008, he worked as a research associate at the GeoVISTA Center at Penn State, where he cofounded the North-East Visualization and Analytics Center (NEVAC) and led the development of several award-winning visual analysis tools. His research combined visualization, human–computer interaction, databases, and data mining to support the exploration of complex data.

Chris was in the prime of life, and his sudden passing has been especially difficult for all who knew and worked with him. His colleagues from IEEE CG&A, the visualization and visual analytics community, NEVAC, and the University of Oklahoma mourn his loss and honor the lasting impact he made on the field and on those around him. Figure 1 shows a portrait of Chris, whose warmth, brilliance, and generosity touched so many. The following tributes reflect the deep respect and admiration he inspired throughout his career.

Essentially all academic research of today relies on analysis of data from a wide range of sources. Several underpinning, and rapidly developing, technologies are supporting the analysis of this data. Visualization serves as an interface to this ecosystem of tools and methods and integrates them into environments supporting scientific workflows, effectively sharing cognitive load between computers and humans. There is, however, a gap between the state-of-the-art in visual data analysis and current wide-spread academic practice. Support for the introduction of new, improved and tailored, visual data analysis environments thus has the potential to address challenges involving large and complex data, creating competitive advantages for researchers. To fill the gap and capitalize on this opportunity, the InfraVis initiative has been created in Sweden with the mission to operate an infrastructure consisting of visualization experts, software solutions, and access to high-end visualization laboratories. Users of InfraVis are offered assistance through a national helpdesk with rapid response times as well as more in-depth projects addressing specific data and software challenges. InfraVis provides software solutions based on development within connected research groups, curation of international software and best practice, and user training in the form of courses, seminars and on-line documentation. To build an infrastructure with national coverage, we have pooled together nine visualization environments in Sweden interconnected in a nodal structure. The nodes are hosted in proximity to research environments in visualization, which enables direct access to the research front as well as to state-of-art facilities. The governance structure of InfraVis is based on the leading researchers in visualization in Sweden as well as an international advisory board.

Similarity-based analysis is a powerful and intuitive tool for exploring large data sets, for instance, for revealing patterns by grouping items by similarity or for recommending items based on selected samples. However, similarity is an abstract and subjective property which makes it hard to evaluate by a purely computational approach. Furthermore, there are usually several possible computational models that could be applied to the data, each with its own strengths and weaknesses. With this in mind, we aim to extend the research frontier regarding what impact the choice of a computational model may have on the results. In this paper, we target the scope of embedding-based similarity calculations on text documents and seek to answer the research question: "How can a better understanding of the continuous similarity distribution captured by different models lead to better similarity calculations on document sets?". We propose a new and generic methodology based on similarity network comparison, and based on this approach, we have developed a computational pipeline together with a prototype visual analytics tool that allows the user to easily assess the level of model agreement/disagreement. To demonstrate the potential of our method, as well as showing its application to real world scenarios, we apply it in an experimental setup using three state-of-the-art text embedding models and three different text corpora. In view of the surprisingly low level of model agreement regarding the data, we also discuss strategies for handling model disagreement.

A dynamic (or temporal) network is a widely used structure that enables understanding dynamic systems by modeling interactions among system components over time. In many real-world cases, however, components (called nodes) and/or interactions (called edges) contain numerous meaningful attributes, leading to the need for a more suitable instrument for representing and analyzing these dynamic and complex systems with multiple attributes: the Dynamic Multivariate Network (DMVN). In this work, we extended LargeNetVis, a visualization system specifically designed for large dynamic networks that focus on network community structure and dynamics, to enable the visual exploration of DMVNs and their communities. The newly introduced visual encodings and interactions allow the visualization of nodes' and edges' attributes at different granularity levels and produce a node tracking capability from both top-down and bottom-up perspectives. With these functionalities, one can track individual nodes across dynamic communities over time. The proposed approach is validated by comparing it with the original LargeNetVis system and conducting a user evaluation involving 37 participants.

The sensemaking process of large sets of text documents is highly challenging for tasks such as obtaining a comprehensive overview or keeping up with the most important trends and topics. Even though several established methods for condensation and summarization of large text corpora exist, many of them lack the ability to account for difference in prevalence between identified topics, which in turn impedes quantitative analysis. In this paper, we therefore propose a novel prevalence-aware method for topic extraction, and show how it can be used to obtain important insights from two text corpora with very different content. We also implemented a prototype visual analytics tool which guides the user in the search for relevant insights and promotes trust in the yielded results. We have verified our application by a user study, as well as by a validation run on a data set with previously known topic structure. The results clearly show that our approach is suitable for text mining, that is can be used by non-experts, and that it offers features which makes it an interesting candidate for use in several different analyze scenarios.