Recently, some works have built adaptive systems providing assistance to the user in virtual reality (VR), with little or no knowledge of the user’s task. These task-blind help systems can influence behaviours and exploration strategies; however, their ability to significantly improve users’ performance on their tasks is still unclear. In this study, we aim to clarify the impact of task-blind help systems on user performance. We also explore two avenues that could provide a better understanding of why these systems can be effective and interesting to study. Our controlled user study involved 56 participants in an immersive analytics environment and compared four VR help-system configurations, including three task-blind systems and a no-assistance baseline. Results showed significant task performance improvements with one task-blind system, highlighting user control as a key factor of efficiency. This work demonstrates the potential of task-blind help systems, offering a flexible framework for adaptive design and raising questions about their broader applications.

The conceptual structures built with Formal Concept Analysis (FCA) and its extensions are appropriate constructs for supporting Exploratory Search (ES). FCA indeed classifies a set of objects described by Boolean attributes in a concept lattice which is prone to (intra-lattice) navigation. Relational Concept Analysis (RCA), for its part, classifies several sets of objects connected through multiple binary relationships by using logical operators (quantifiers) which can be approximate. The output is a set of interconnected concept lattices, thus adding inter-lattice navigation opportunities. In this paper, we describe the web platform RCAviz, which aims to support such intra- and inter-lattice navigation. The user can select a subset of objects and attributes as a starting point for navigation. Then RCAviz shows the associated concept and its close intra- and inter-lattice neighbors. The user can access to the objects and attributes introduced and inherited in a concept. They then can navigate, i.e. zoom and pan the current view, and move from one concept to another. Additional views show the previous and the next conceptual structures, as well as an history which allows the user to browse its navigation. A navigation example is shown on a real dataset to illustrate the potential of RCAviz for ES.

Cartographers have long been interested in the representation of various movements such as migration, commercial exchanges and transportation. There are several techniques for visualizing this information; this paper focuses on flow mapping. A flow map shows a set of movements through line symbols connecting an origin to a destination. Each link is associated with a value that corresponds to the volume of the movement. However, once data reach a certain volume, the maps quickly become cluttered and can be difficult to read and understand. Moreover, the values of the movements must be correctly represented to avoid inducing biased interpretations. The objective of this paper is to create flow maps displaying flows of highly variable thicknesses so that the associated values are correctly represented. The technique used to create the flow paths does not create crossings between flows. In order to remove any visual clutter, such as overlaps between flows and geographic features, some areas of the map are distorted. In other words, our method of map distortion adapts the polygon vector base map to the flows, the central information of the visualization, and not the other way around.

While neural networks (NN) have been successfully applied to many NLP tasks, the way they function is often difficult to interpret. In this article, we focus on binary text classification via NNs and propose a new tool, which includes a visualization of the decision boundary and the distances of data elements to this boundary. This tool increases the interpretability of NN. Our approach uses two innovative views: (1) an overview of the text representation space and (2) a local view allowing data exploration around the decision boundary for various localities of this representation space. These views are integrated into a visual platform, EBBE-Text, which also contains state-of-the-art visualizations of NN representation spaces and several kinds of information obtained from the classification process. The various views are linked through numerous interactive functionalities that enable easy exploration of texts and classification results via the various complementary views. A user study shows the effectiveness of the visual encoding and a case study illustrates the benefits of using our tool for the analysis of the classifications obtained with several recent NNs and two datasets.

The mapping of the damage caused by natural disasters is a crucial step in deciding on the actions to take at the international, national, and local levels. The large variety of representations that we have observed leads to problems of transfer and variations in analysis. In this article, we propose a representation, Regular Dot map (RD), and we compare it to 4 others routinely used to visualise post-disaster damage. Our comparison is based on a user study in which a set of participants carried out various tasks on multiple datasets using the various visualisations. We then analysed the behaviour during the experiment using three approaches: (1) quantitative analysis of user answers according to the reality on the ground, (2) quantitative analysis of user preferences in terms of perceived effectiveness and appearance, and (3) qualitative analysis of the data collected using an eye tracker. The results of this study lead us to believe that RD is the best compromise in terms of effectiveness among the various representations studied.

Many real world data can be modeled by a graph with a set of nodes interconnected to each other by multiple relationships. Such a rich graph is called multilayer graph or network. Providing useful visualization tools to support the query process for such graphs is challenging. Although many approaches have addressed the visual query construction, few efforts have been done to provide a contextualized exploration of query results and suggestion strategies to refine the original query. This is due to several issues such as i) the size of the graphs ii) the large number of retrieved results and iii) the way they can be organized to facilitate their exploration. In this article, we present VERTIGo, a novel visual platform to query, explore and support the analysis of large multilayer graphs. VERTIGo provides coordinated views to navigate and explore the large set of retrieved results at different granularity levels. In addition, the proposed system supports the refinement of the query by visual suggestions to guide the user through the exploration process. Two examples and a user study demonstrate how VERTIGo can be used to perform visual analysis (query, exploration, and suggestion) on real world multilayer networks.

Many real world data can be represented by a network with a set of nodes linked each other by multiple relations. Such a rich graph is called multilayer graph. In this demo, we present a tool for Visual Querying of Large Multilayer Graphs that allows to visually draw the query, retrieve result patterns and finally navigate and browse the results considering the original multilayer graph database. Our approach does not only provide a graphical user interface for the graph engine but the query processing is fully integrated.

In the recent years, there has been a massive increase in the amount of data being produced about human and animal health related events. Epidemiologists have to analyze this epidemiological data on a regular basis. They use this spatio-temporal information, most of which is shared online, to detect, observe, and track geographic locations of disease outbreaks over time. Unfortunately, manually retrieving the data from a website like Google News and then deriving sensible insights from the huge dataset consumes a lot of time and effort. We present EpidNews, a new visual analytics tool that helps to visualize and explore epidemiological news data for animals. The tool uses several views depicting various levels of abstraction, which helps fulfill almost all the data analysis requirements of epidemiologists. We also present the case study of an epidemiology expert, wherein she assesses the usability and productivity of EpidNews by using the tool in her daily work.

Multiple time series are a set of multiple quantitative variables occurring at the same interval. They are present in many domains such as medicine, finance, and manufacturing for analytical purposes. In recent years, streamgraph visualization (evolved from ThemeRiver) has been widely used for representing temporal evolution patterns in multiple time series. However, streamgraph as well as ThemeRiver suffer from scalability problems when dealing with several time series. To solve this problem, multiple time series can be organized into a hierarchical structure where individual time series are grouped hierarchically according to their proximity. In this paper, we present a new streamgraph-based approach to convey the hierarchical structure of multiple time series to facilitate the exploration and comparisons of temporal evolution. Based on a focus+context technique, our method allows time series exploration at different granularities (e.g., from overview to details). To illustrate our approach, two usage examples are presented.

Energy based algorithms are powerful techniques for laying out graphs. They tend to generate aesthetically pleasing graph embeddings, exhibiting symmetries and community structures. When dealing with large graphs, an important drawback of these algorithms is to produce embeddings where many nodes overlap, leading to cluttering issues. While several approaches have been proposed for node overlap removal on 2D graph layouts, to the best of our knowledge, there is no work dedicated to 1D graph layouts. In this paper, we first define 4 requirements for 1D graph node overlap removal. Then, we propose a O(|V|log(|V|)) time algorithm meeting these requirements. We illustrate our approach with two case studies based on arc diagrams where nodes are positioned by applying a MDS technique to highlight community structures. Finally, we compare our technique with alternatives from 2D graph techniques, and a discussion highlights some properties of the results.

The problem of local community detection refers to the identification of a community starting from a query node and using limited information about the network structure. Existing methods for solving this problem however are not designed to deal with multilayer network models, which are becoming pervasive in many fields of science. In this work, we present the first method for local community detection in multilayer networks. Our method exploits both internal and external connectivity of the nodes in the community being constructed for a given seed, while accounting for different layer-specific topological information. Evaluation of the proposed method has been conducted on real-world multilayer networks.

Many real world information can be represented by a graph with a set of nodes interconnected with each other by multiple type of relations called edge layers (e.g., social network, biological data). Edge bundling techniques have been proposed to solve cluttering issue for standard graphs while few efforts were done to deal with the similar issue for multilayer graphs. In multilayer graphs scenario, not only the clutter induced by large amount of edges is a problem but also the fact that different type of edges can overlap each other making useless the final visualization. In this paper we introduce a new multilayer graph edge bundling technique that firstly produces a preliminary edge bundling independently of the different edge layers and then deals with the specificity of multilayer graphs where more than one type of edges can be routed on the same bundle. The proposed visualization is tested on a real world case study and the outcomes point out the ability of our proposal to discover patterns present in the data.

The advancement of motion sensing input devices has enabled the collection of multivariate time-series body movement data. Analyzing such type of data is challenging due to the large amount of data and the task of mining for interesting temporal movement patterns. To address this problem, we propose an interface to visualize and analyze body movement data. This visualization enables users to navigate and explore the evolution of movement over time for different movement areas. We also propose a clustering method based on hierarchical clustering to group similar movement patterns. The proposed visualization is illustrated with a case study which demonstrates the ability of the interface to analyze body movements.

In this work, we introduce SentiCompass for exploring and comparing the sentiments of time-varying Twitter data. Our visualization design combines 2D psychology model of affect (i.e. emotion) with a time tunnel representation. To illustrate our visualization design, two case studies are conducted. They demonstrate the effectiveness of SentiCompass in achieving various tasks related to temporal sentiment and affective analysis of tweets. The interactive demo of our system is available at: http://youtu.be/ZaMF6VNO7tA

Recent advances in network science allows the modeling and analysis of complex inter-related entities. These entities often interact with each other in a number of different ways. Simple graphs fail to capture these multiple types of relationships requiring more sophisticated mathematical structures. One such structure is multigraph, where entities (or nodes) can be linked to each other through multiple edges. In this paper we describe a new method to manage multiple types of relationships existing in multigraphs. Our approach is based on the concept of pair of nodes (edges) and, in particular, we study how nodes on different layers interact which each other considering the edges they share. We propose a two level strategy that summarizes global/local multigraph features. The global view helps us to gain knowledge related to the characteristics of layers and how they interact while the local view provides an analysis of individual layers highlighting edge properties such as cluster structure. Our proposal is complementary to standard node-link diagram and it can be coupled with such techniques in order to intelligently explore multigraphs. The proposed visualization is tested on a real world case study and the outcomes point out the ability of our proposal to discover patterns present in the data.

Large dynamic graphs occur in many fields. While overviews are often used to provide summaries of the overall structure of the graph, they become less useful as data size increases. Often analysts want to focus on a specific part of the data according to domain knowledge, which is best suited by a bottom-up approach. This paper presents an egocentric, bottom-up method to exploring a large dynamic network using a storyline representation to summarise localized behavior of the network over time.

The emergence of very large hierarchies that result from the increase in available data raises many problems of visualization and navigation. On data sets of such scale, classical graph drawing methods do not take advantage of certain human cognitive skills such as shape recognition. These cognitive skills could make it easier to remember the global structure of the data. In this paper, we propose a method that is based on the use of nested irregular shapes. We name it GosperMap as we rely on the use of a Gosper Curve to generate these shapes. By employing human perception mechanisms that were developed by handling, for example, cartographic maps, this technique facilitates the visualization and navigation of a hierarchy. An algorithm has been designed to preserve region containment according to the hierarchy and to set the leaves' sizes proportionally to a property, in such a way that the size of nonleaf regions corresponds to the sum of their children's sizes. Moreover, the input ordering of the hierarchy's nodes is preserved, i.e., the areas that represent two consecutive children of a node in the hierarchy are adjacent to one another. This property is especially useful because it guarantees some stability in our algorithm. We illustrate our technique by providing visualization examples of the repartition of tax money in the US over time. Furthermore, we validate the use of the GosperMap in a professional documentation context and show the stability and ease of memorization for this type of map.

The modern age has seen an exponential growth of social network data available on the web. Analysis of these networks reveal important structural information about these networks in particular and about our societies in general. More often than not, analysis of these networks is concerned in identifying similarities among social networks and how they are different from other networks such as protein interaction networks, computer networks and food web. In this paper, our objective is to perform a critical analysis of different social networks using structural metrics in an effort to highlight their similarities and differences. We use five different social network datasets which are contextually and semantically different from each other. We then analyze these networks using a number of different network statistics and metrics. Our results show that although these social networks have been constructed from different contexts, they are structurally similar.

The emergence of scale free and small world properties in real world complex networks has stimulated lots of activity in the field of network analysis. An example of such a network comes from the field of Content Analysis (CA) and Text Mining where the goal is to analyze the contents of a set of web pages. The Network can be represented by the words appearing in the web pages as nodes and the edges representing a relation between two words if they appear in a document together. In this paper we present a CA system that helps users analyze these networks representing the textual contents of a set of web pages visually. Major contributions include a methodology to cluster complex networks based on duplication of nodes and identification of bridges i.e. words that might be of user interest but have a low frequency in the document corpus. We have tested this system with a number of data sets and users have found it very useful for the exploration of data. One of the case studies is presented in detail which is based on browsing a collection of web pages on Wikipedia.

Visual graph analytics definitely relies on the use of node and edge metrics to identify salient properties in graphs. Most often, these metrics are turned into useful visual cues, or are used to interactively filter out parts of a graph while querying it, for instance. Along the years, analysts coming from different application domains have designed metrics to serve specific needs. Graph analytics, sometimes also called network science, recently developed as a cross-discipline field developing models shared by numerous application domains such as bio-informatics, social network analysis, web graphs, etc.[4] [10]. As a consequence, we end up finding various metrics in the literature aiming at similar goals; different names and analytics description often hide similarity between two metrics that originated from different fields. We survey a list of edge metrics for graphs and compare their relative value and behaviour, in an effort to organize them into a taxonomy and underline the genuine ingredients in each of them disregarding their origin.