Physicians work at a very tight schedule and need decision-making support tools to help on improving and doing their work in a timely and dependable manner. Examining piles of sheets with test ...results and using systems with little visualization support to provide diagnostics is daunting, but that is still the usual way for the physicians' daily procedure, especially in developing countries. Electronic Health Records systems have been designed to keep the patients' history and reduce the time spent analyzing the patient's data. However, better tools to support decision-making are still needed. In this article, we propose ClinicalPath, a visualization tool for users to track a patient's clinical path through a series of tests and data, which can aid in treatments and diagnoses. Our proposal is focused on patient's data analysis, presenting the test results and clinical history longitudinally. Both the visualization design and the system functionality were developed in close collaboration with experts in the medical domain to ensure a right fit of the technical solutions and the real needs of the professionals. We validated the proposed visualization based on case studies and user assessments through tasks based on the physician's daily activities. Our results show that our proposed system improves the physicians' experience in decision-making tasks, made with more confidence and better usage of the physicians' time, allowing them to take other needed care for the patients.
Temporal (or time-evolving) networks are commonly used to model complex systems and the evolution of their components throughout time. Although these networks can be analyzed by different means, ...visual analytics stands out as an effective way for a pre-analysis before doing quantitative/statistical analyses to identify patterns, anomalies, and other behaviors in the data, thus leading to new insights and better decision-making. However, the large number of nodes, edges, and/or timestamps in many real-world networks may lead to polluted layouts that make the analysis inefficient or even infeasible. In this paper, we propose LargeNetVis, a web-based visual analytics system designed to assist in analyzing small and large temporal networks. It successfully achieves this goal by leveraging three taxonomies focused on network communities to guide the visual exploration process. The system is composed of four interactive visual components: the first (Taxonomy Matrix) presents a summary of the network characteristics, the second (Global View) gives an overview of the network evolution, the third (a node-link diagram) enables community- and node-level structural analysis, and the fourth (a Temporal Activity Map - TAM) shows the community- and node-level activity under a temporal perspective. We demonstrate the usefulness and effectiveness of LargeNetVis through two usage scenarios and a user study with 14 participants.
Information Visualisation strategies can be applied in a variety of domains. In the context of temporal networks, i.e., networks in which interactions between individuals occur throughout time, ...efforts have been conducted to develop visual approaches that allow finding interaction patterns, anomalies, and other behaviours not previously perceived in the data. This paper presents two case studies involving real-world education networks from a primary school and a high school. For this purpose, we used the Massive Sequence View (MSV) layout with the Community-based Node Ordering (CNO) method, two well established approaches for visual analysis of temporal networks. Our results show that the identified patterns involving students/students and students/teachers represent important information to benefit and support decision making about school management and teaching strategies, especially those related to strategic group formation.
•An online and nonuniform timeslicing strategy that enhances temporal and streaming network visualization.•Timeslicing reduces visual clutter and optimizes global pattern identification.•Evaluation ...through case studies using two real-world networks with distinct characteristics.
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Visual analysis of temporal networks comprises an effective way to understand the network dynamics. It facilitates the identification of patterns, anomalies, and other network properties, thus resulting in fast decision making. The amount of data in real-world networks, however, may result in a layout with high visual clutter due to edge overlapping. This is particularly relevant in the so-called streaming networks, in which edges are continuously arriving (online) and in non-stationary distribution. All three network dimensions, namely node, edge, and time, can be manipulated to reduce such clutter and improve readability. This paper presents an online and nonuniform timeslicing method that enhances temporal and streaming network analyses. We conducted experiments using two real-world networks to compare our method against uniform and nonuniform timeslicing strategies. The results show that our method automatically selects timeslices that effectively reduce visual clutter in periods with bursts of events. As a consequence, decision making based on the identification of global temporal patterns becomes faster and more reliable.
In the last decades, urbanization and population growth substantially increased water consumption for agricultural, industrial, and residential purposes. Characterizing the interplay between ...environmental variables and water resources plays a critical role in establishing effective water management policies. In this paper, we apply Canonical Correlation Analysis (CCA) in a set of climate and hydrological indicators to investigate the behavior of these environmental variables over time in different geographical regions of California, as well as the relationship among these regions. CCA served as a base to establish a temporal graph that models the relationship between the stations over time, and advanced graph visualization techniques are used to produce patterns that aid in the comprehension of the underlying phenomena. Our results identified important temporal patterns, such as heterogeneous behavior in the dry season and lower correlation between the stations in
La Niña
years. We show that the combination of CCA and visual analytics can assist water experts in identifying important climate and hydrological events in different scenarios.
Networks are often used to model the structure of interactions between parts of a system. One important characteristic of a network is the so-called network community structures that are groups of ...nodes more connected between themselves than with nodes from other groups. Such community structure is fundamental to better understand the organization of networks. Although there are several community detection algorithms in the literature, choosing the most appropriate for a specific task is not always trivial. This paper introduces a methodology to analyze the performance of community detection algorithms using network visualization. We assess the methodology using two widely adopted community detection algorithms: Infomap and Louvain. We apply both algorithms to four real-world networks with a variety of characteristics to demonstrate the usefulness and generality of the methodology. We discuss the performance of these algorithms and show how the user may use statistical and visual analytics to identify the most appropriate network community detection algorithm for a certain network analysis task.
Visualization strategies facilitate streaming network analysis by allowing its exploration through graphical and interactive layouts. Depending on the strategy and the network density, such layouts ...may suffer from a high level of visual clutter that hides meaningful temporal patterns, highly active groups of nodes, bursts of activity, and other important network properties. Edge sampling improves layout readability, highlighting important properties and leading to easier and faster pattern identification and decision making. This paper presents
Streaming Edge Sampling for Network Visualization
–SEVis, a streaming edge sampling method that discards edges of low-active nodes while preserving a distribution of edge counts that is similar to the original network. It can be applied to a variety of layouts to enhance streaming network analyses. We evaluated SEVis performance using synthetic and real-world networks through quantitative and visual analyses. The results indicate a higher performance of SEVis for clutter reduction and pattern identification when compared with other sampling methods.
Temporal networks are widely used to map phenomena into complex systems in several research disciplines, such as computer science, business, and biology. Several layouts can be used in visual ...analyses of temporal networks. The identification of the most suitable for a given task is, however, not trivial. This paper presents a user study that analyzes the performance of four different layouts: Massive Sequence View (MSV), Temporal Activity Map, matrix animation, and structural animation, when applied to pattern detection tasks of time-evolving networks. Our results show that all four layouts are appropriate to perform the evaluated tasks; however, the structural animation and MSV scored higher across different types of users.
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Temporal networks have been widely used to model instances of a domain of interest and their time-evolving interaction, including modeling individuals and face-to-face contacts throughout time. In ...the context of infection spread, such individuals can, e.g., remain susceptible, recovered, or be infected at a particular time. Understanding the infection spread behavior (its speed and magnitude, for instance) is crucial for quick and reliable decision making. Network visualization strategies can help in this task as they allow easy identification of who infected whom and when, epidemics outbreak, and other relevant aspects. This paper presents a visualization approach for the simulation and analysis of infection spread dynamics that considers different infection probabilities and different levels of social distancing (inter-group interaction). We performed quantitative and visual experiments using three real-world social networks with distinct characteristics and from two different environments. Our findings reveal the overall influence of different levels of inter-group interaction and infection probabilities in the infection spread dynamics and also demonstrate the usefulness of our approach for enhanced local (individual- or group-level) investigations.
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