Alessio Arleo

Information Diffusion (ID) describes how a value (e.g., a pathogen, a rumor, a packet) spreads through an underlying “medium” network of elements (e.g., a social or computer network). Understanding the information diffusion process is essential to predicting trends, controlling misinformation, and enhancing decision‐making as well as communication strategies. Visual Analytics has shown significant potential in supporting comprehension of ID processes in several domains. These approaches vary greatly in their design, both in terms of data and visual encodings. The variety of designs and application domains motivates this survey, which complements existing surveys with a focus on visualization of transmission processes in contrast to other surveys about visualizing networks. This survey defines types of transmission and medium networks, identifies common visualization principles, categorizes them, and identifies gaps–opportunities for future research.

Dimensionality Reduction (DR) techniques are commonly used for the visual exploration and analysis of high-dimensional data due to their ability to project datasets of high-dimensional points onto the 2D plane. However, projecting datasets in lower dimensions often entails some distortion, which is not necessarily easy to recognize but can lead users to misleading conclusions. Several Projection Quality Metrics (PQMs) have been developed as tools to quantify the goodness-of-fit of a DR projection; however, they mostly focus on measuring how well the projection captures the global or local structure of the data, without taking into account the visual distortion of the resulting plots, thus often ignoring the presence of outliers or artifacts that can mislead a visual analysis of the projection. In this work, we introduce the Warping Index (WI), a new metric for measuring the quality of DR projections onto the 2D plane, based on the assumption that the correct preservation of empty regions between points is of crucial importance towards a faithful visual representation of the data.

Protein folding is the process by which proteins go from a linear chain of amino acids to a 3-D structure that determines their biological function. Although recent advances in protein 3-D structure prediction can directly determine the folded protein’s final shape, the process by which this happens is complex and not very well understood. Part of the study of protein folding focuses on the analysis of their “energy landscape,” defined by the molecule’s energy as a function of its structure. The data are mostly obtained through atomic-level computer simulations and are very high-dimensional, making them difficult to interpret. Visualization can be a powerful tool to support researchers studying the energy landscape of proteins; however, we noticed that they are not widely adopted by the scientific community. We present the main methods currently used and the challenges they face, as well as future opportunities for visualization in this field.

Dashboards have arguably been the most used visualizations during the COVID-19 pandemic. They were used to communicate its evolution to national governments for disaster mitigation, to the public domain to inform about its status, and to epidemiologists to comprehend and predict the evolution of the disease. Each design had to be tailored for different tasks and to varying audiences—in many cases set up in a very short time due to the urgent need. In this article, we collect notable examples of dashboards and reflect on their use and design during the pandemic from a user-oriented perspective. We interview a group of researchers with varying visualization expertise who actively used dashboards during the pandemic as part of their daily workflow. We discuss our findings and compile a list of lessons learned to support future visualization researchers and dashboard designers.

In the vast landscape of visualization research, Dimensionality Reduction (DR) and graph analysis are two popular subfields, often essential to most visual data analytics setups. DR aims to create representations to support neighborhood and similarity analysis on complex, large datasets. Graph analysis focuses on identifying the salient topological properties and key actors within network data, with specialized research investigating how such features could be presented to users to ease the comprehension of the underlying structure. Although these two disciplines are typically regarded as disjoint subfields, we argue that both fields share strong similarities and synergies that can potentially benefit both. Therefore, this paper discusses and introduces a unifying framework to help bridge the gap between DR and graph (drawing) theory. Our goal is to use the strongly math‐grounded graph theory to improve the overall process of creating DR visual representations. We propose how to break the DR process into well‐defined stages, discuss how to match some of the DR state‐of‐the‐art techniques to this framework, and present ideas on how graph drawing, topology features, and some popular algorithms and strategies used in graph analysis can be employed to improve DR topology extraction, embedding generation, and result validation. We also discuss the challenges and identify opportunities for implementing and using our framework, opening directions for future visualization research.

In the vast landscape of visualization research, Dimensionality Reduction (DR) and graph analysis are two popular subfields, often essential to most visual data analytics setups. DR aims to create representations to support neighborhood and similarity analysis on complex, large datasets. Graph analysis focuses on identifying the salient topological properties and key actors within networked data, with specialized research on investigating how such features could be presented to the user to ease the comprehension of the underlying structure. Although these two disciplines are typically regarded as disjoint subfields, we argue that both fields share strong similarities and synergies that can potentially benefit both. Therefore, this paper discusses and introduces a unifying framework to help bridge the gap between DR and graph (drawing) theory. Our goal is to use the strongly math-grounded graph theory to improve the overall process of creating DR visual representations. We propose how to break the DR process into well-defined stages, discussing how to match some of the DR state-of-the-art techniques to this framework and presenting ideas on how graph drawing, topology features, and some popular algorithms and strategies used in graph analysis can be employed to improve DR topology extraction, embedding generation, and result validation. We also discuss the challenges and identify opportunities for implementing and using our framework, opening directions for future visualization research.

In temporal ( event-based ) networks, time is a continuous axis, with real-valued time coordinates for each node and edge. Computing a layout for such graphs means embedding the node trajectories and edge surfaces over time in a $2D + t$ space, known as the space-time cube. Currently, these space-time cube layouts are visualized through animation or by slicing the cube at regular intervals. However, both techniques present problems such as below-average performance on tasks as well as loss of precision and difficulties in selecting timeslice intervals. In this paper, we present TimeLighting , a novel visual analytics approach to visualize and explore temporal graphs embedded in the space-time cube. Our interactive approach highlights node trajectories and their movement over time, visualizes node “aging”, and provides guidance to support users during exploration by indicating interesting time intervals (“when”) and network elements (“where”) are located for a detail-oriented investigation. This combined focus helps to gain deeper insights into the temporal network's underlying behavior. We assess the utility and efficacy of our approach through two case studies and qualitative expert evaluation. The results demonstrate how TimeLighting supports identifying temporal patterns, extracting insights from nodes with high activity, and guiding the exploration and analysis process.

In the early 2000s, the transition to paperless documentation of patients’ health data begun at large scale, with the introduction of Electronic Health and Medical Records (EHR and EMR, respectively). This constituted a paradigm shift in how patient data was stored and exchanged among institutions. The impact of the so-called “Electronic Health Revolution” ¹ was significant. Standardization of personal health data allowed for a more uniform definition of diagnoses and their ensuing clinical process, with fewer mistakes in diagnosis and treatment, and a more reliable application of medical guidelines. ² For instance, in the United States (US), patients now have control over their information, with more mandated electronic access. ³ Recent studies showed that online medical records by US adults doubled over the last 8 years. ⁴ Simultaneously, a new generation of smart, affordable, and wearable devices, such as smartwatches, has emerged. These devices generate fine-grained and continuous data about the health status of their users, with minimal discomfort, eliminating the need for specialized equipment. The rapid evolution of Artificial Intelligence (AI) technologies is about to significantly impact healthcare as well. AI technologies present opportunities and challenges for both physicians and patients. ⁵ AI models recognize patterns in complex datasets, potentially identifying a broader range of disease progression patterns that might not be immediately apparent to clinicians or patients. However, the inherent “black-box” nature of AI has slowed its adoption, as healthcare professionals often struggle to evaluate the underlying process that led to the AI recommendations. In essence, while it can be impressive what AI models predict, concerns remain about why the AI produces a particular output, and how. The considerable lack of transparency impedes trust-building, such that “the doctor just won’t accept that,” ⁶ calling for explainable AI output.

Knowledge-intensive processes represent a particularly challenging scenario for process mining. The flexibility that such processes allow constitutes a hurdle as they are hard to capture in a single model. To tackle this problem, multiple visual representations of the same processes could be beneficial, each addressing different information dimensions according to the specific needs and background knowledge of the concrete process workers and stakeholders. In this paper, we propose, describe, and evaluate a framework, named , that leverages visual analytics for the interactive visualization of multi-faceted process information, aimed at supporting the investigation and insight generation of users in their process analysis tasks. is based on a multi-layer visualization methodology that includes a visual backdrop that provides context and an arbitrary number of superimposed and on-demand dimension layers. This arrangement allows our framework to display process information from different perspectives and to project this information onto a domain-friendly representation of the context in which the process unfolds. We provide an in-depth description of the approach’s founding principles, deeply rooted in visualization research, that justify our design choices for the whole framework. We demonstrate the feasibility of the framework through its application in two use-case scenarios in the context of healthcare and personal information management. Plus, we conducted qualitative evaluations with potential end users of both scenarios, gathering precious insights about the efficacy and applicability of our framework to various application domains.

Co-adaptive guidance in visual analytics is a mixed-initiative process in which both the user and the system work together to support each other in solving a given analysis task. While previous studies show the effectiveness of guidance, the impact of guidance design decisions (e.g., the suggestions’ timing, contextualization, or adaptation) and misguidance often remain under-investigated. To investigate these aspects and examine a variety of guidance interaction patterns in a realistic analysis scenario, we present a Wizard of Oz (WOz) study setup in which pairs of participants take the user's and the system's roles, respectively. As users perform their analysis tasks, they are observed by wizards who provide just-in-time guidance as they see fit. Moreover, we designed the study so that wizards would occasionally and unknowingly provide misguidance during the analysis to investigate the users’ confidence in guidance systems. We recruited two groups of participants (12 wizards and 12 users) and paired each participant with two from the other group, obtaining 48 observations. We report insights on interactions between users and wizards. By analyzing these interaction dynamics and the guidance strategies the wizards apply, we derive recommendations for implementing and evaluating future co-adaptive guidance systems.