Paolo Federico

A visual analytics approach to dynamic social networks

The visualization and analysis of dynamic networks have become increasingly important in several fields, for instance sociology or economics. The dynamic and multi-relational nature of this data poses the challenge of understanding both its topological structure and how it changes over time. In this paper we propose a visual analytics approach for analyzing dynamic networks that integrates: a dynamic layout with user-controlled trade-off between stability and consistency; three temporal views based on different combinations of node-link diagrams (layer superimposition, layer juxtaposition, and two-and-a-half-dimensional view); the visualization of social network analysis metrics; and specific interaction techniques for tracking node trajectories and node connectivity over time. This integration of visual, interactive, and automatic methods supports the multi-faceted analysis of dynamically changing networks.

Visual Encodings of Temporal Uncertainty: A Comparative User Study

A number of studies have investigated different ways of visualizing uncertainty. However, in the temporal dimension, it is still an open question how to best represent uncertainty, since the special characteristics of time require special visual encodings and may provoke different interpretations. Thus, we have conducted a comprehensive study comparing alternative visual encodings of intervals with uncertain start and end times: gradient plots, violin plots, accumulated probability plots, error bars, centered error bars, and ambiguation. Our results reveal significant differences in error rates and completion time for these different visualization types and different tasks. We recommend using ambiguation - using a lighter color value to represent uncertain regions - or error bars for judging durations and temporal bounds, and gradient plots - using fading color or transparency - for judging probability values.

Visual Analysis of Dynamic Networks Using Change Centrality

The visualization and analysis of dynamic social networks are challenging problems, demanding the simultaneous consideration of relational and temporal aspects. In order to follow the evolution of a network over time, we need to detect not only which nodes and which links change and when these changes occur, but also the impact they have on their neighbourhood and on the overall relational structure. Aiming to enhance the perception of structural changes at both the micro and the macro level, we introduce the change centrality metric. This novel metric, as well as a set of further metrics we derive from it, enable the pair wise comparison of subsequent states of an evolving network in a discrete-time domain. Demonstrating their exploitation to enrich visualizations, we show how these change metrics support the visual analysis of network dynamics.

A Survey on Visual Approaches for Analyzing Scientific Literature and Patents

The increasingly large number of available writings describing technical and scientific progress, calls for advanced analytic tools for their efficient analysis. This is true for many application scenarios in science and industry and for different types of writings, comprising patents and scientific articles. Despite important differences between patents and scientific articles, both have a variety of common characteristics that lead to similar search and analysis tasks. However, the analysis and visualization of these documents is not a trivial task due to the complexity of the documents as well as the large number of possible relations between their multivariate attributes. In this survey, we review interactive analysis and visualization approaches of patents and scientific articles, ranging from exploration tools to sophisticated mining methods. In a bottom-up approach, we categorize them according to two aspects: (a) data type (text, citations, authors, metadata, and combinations thereof), and (b) task (finding and comparing single entities, seeking elementary relations, finding complex patterns, and in particular temporal patterns, and investigating connections between multiple behaviours). Finally, we identify challenges and research directions in this area that ask for future investigations.

How Do You Connect Moving Dots? Insights from User Studies on Dynamic Network Visualizations

In recent years, the analysis of dynamic network data has become an increasingly prominent research issue. While several visual analytics techniques with the focus on the examination of temporal evolving networks have been proposed in recent years, their effectiveness and utility for end users need to be further analyzed. When dealing with techniques for dynamic network analysis, which integrate visual, computational, and interactive components, users become easily overwhelmed by the amount of information displayed—even in case of small sized networks. Therefore we evaluated visual analytics techniques for dynamic networks during their development, performing intermediate evaluations by means of mock-up and eye-tracking studies and a final evaluation of the running interactive prototype, traceing three pathways of development in detail: The first one focused on the maintenance of the user’s mental map throughout changes of network structure over time, changes caused by user interactions, and changes of analytical perspectives. The second one addresses the avoidance of visual clutter, or at least its moderation. The third pathway of development follows the implications of unexpected user behaviour and multiple problem solving processes. Aside from presenting solutions based on the outcomes of our evaluation, we discuss open and upcoming problems and set out new research questions.

Qualizon graphs: space-efficient time-series visualization with qualitative abstractions

In several application fields, the joint visualization of quantitative data and qualitative abstractions can help analysts make sense of complex time series data by associating precise numeric values with corresponding domain-specific interpretations, such as good, bad, high, low, normal. At the same time, the need to analyse large multivariate time-oriented datasets often calls for keeping visualizations as compact as possible. In this paper, we introduce Qualizon Graphs, a compact visualization that combines quantitative data and qualitative abstractions. It is based on the well known Horizon Graphs, but instead of a predefined number of equally sized bands, it uses as many bands as qualitative categories with corresponding different sizes. In this way, Qualizon Graphs increase the data density of visualized quantitative values and inherently integrate qualitative abstractions. A user study shows that Qualizon Graphs are as fast and accurate as Horizon Graphs for quantitative data, and are an alternative to state-of-the-art visualizations for both quantitative and qualitative data, enabling a trade-off between speed and accuracy.

Visual Analysis of Compliance with Clinical Guidelines

Clinical guidelines provide recommendations in the form of applicable actions in a specific clinical context. Computer Interpretable Guidelines (CIG) aim to achieve guideline integration into clinical practice to increase health care quality. Analyzing the compliance with a CIG can facilitate the implementation and assist in the design of CIGs, but to help medical experts in the detection of patterns in the wealth of the data is a challenging task. We suggest an approach based on visual analytics, intertwining interactive visualization and automated data analysis i.e. analysis of compliance with a CIG. Our solution covers highlighting and abstraction for time-oriented patient parameters, and aggregation of repeatedly missing actions into intervals; in addition valid, invalid, and missing actions are represented visually. Furthermore, we discuss a case study showing how the applied techniques can assist in the detection of interesting patterns.

Vertigo zoom: combining relational and temporal perspectives on dynamic networks

A well-designed visualization of dynamic networks has to support the analysis of both temporal and relational features at once. In particular to solve complex synoptic tasks, the users need to understand the topological structure of the network, its evolution over time, and possible interdependencies. In this paper, we introduce the application of the vertigo zoom interaction technique, derived from filmmaking, to information visualizations. When applied to a two-and-a-half-dimensional view, this interaction technique enables smooth transitions between the relational perspective (node-link diagrams and scatter plots) and the time perspective (trajectories and line charts), supporting a seamless visual analysis and preserving the users mental map.

Gnaeus: utilizing clinical guidelines for knowledge-assisted visualisation of EHR cohorts

The advanced visualization of electronic health records (EHRs), supporting a scalable analysis from single patients to cohorts, intertwining patients’ conditions with executed treatments, and handling the complexity of timeoriented data, is an open challenge of visual analytics for health care. We propose an approach that, according to the knowledge-assisted visualization paradigm, leverages the domain knowledge acquired by clinical experts and formalized into computer-interpretable guidelines (CIGs), in order to improve the automated analysis, the visualization, and the interactive exploration of EHRs of patient cohorts. In this way, the analyst can get insights about the clinical history of multiple patients and assess the effectiveness of their health care treatments.

Visual Analytics of Electronic Health Records with a Focus on Time

Visual Analytics is a field of computer science that deals with methods to perform data analysis using both computer-based methods and human judgment facilitated by direct interaction with visual representations of data. Electronic health record systems that apply Visual Analytics methods have the potential to provide healthcare stakeholders with much-needed cognitive support in exploring and querying records. This chapter presents Visual Analytics projects addressing five particular challenges of electronic health records: (1) The complexity of time-oriented data constitutes a cross-cutting challenge so that all projects need to consider design aspects of time-oriented data in one way or another. (2) As electronic health records encompass patient conditions and treatment, they are inherently heterogeneous data. (3) Scaling from single patients to cohorts requires approaches for relative time, space efficiency, and aggregation. (4) Data quality and uncertainty are common issues that need to be considered in real-world projects. (5) A user-centered design process and suitable interaction techniques are another cross-cutting challenge for each and every Visual Analytics project.