The tasks carried out by modern information workers become increasingly complex and time-consuming. They often require to evaluate, interpret, and compare information from different sources presented in multiple application windows. With large, high resolution displays, multiple application windows can be arranged in a way so that a large amount of information is visible simultaneously. However, individual application windows ’ contents and visual representations are isolated and relations between information items contained in these windows are not explicit. Thus, relating and comparing information across applications has to be executed manually by the user, which is a tedious and error-prone task. In this paper we present visual links connecting related pieces of information across application windows and thereby guiding the user’s attention to relevant information. Applications are coordinated by a management application accessible via a light-weight interface. User selections are synchronized across registered applications and visual links are rendered on top of the desktop content by a window manager. Initial user feedback was very positive and indicates that visual links improve task efficiency when analyzing information from multiple sources. visual links, information analysis, multiple coordi-Keywords: nated views

Abstract. In Lombardi drawings of graphs, edges are represented as circular arcs, and the edges incident on vertices have perfect angular resolution. However, not every graph has a Lombardi drawing, and not every planar graph has a planar Lombardi drawing. We introduce k-Lombardi drawings, in which each edge may be drawn with k circular arcs, noting that every graph has a smooth 2-Lombardi drawing. We show that every planar graph has a smooth planar 3-Lombardi drawing and further investigate topics connecting planarity and Lombardi drawings. 1

Recent empirical research has indicated that human graph reading performance improves when crossing angles increase. However, crossing angle has not been used as an aesthetic criterion for graph drawing algorithms so far. In this paper, we introduce a force-directed method that aims to construct graph drawings with large crossing angles. Experiments indicate that our method significantly increases crossing angles. Surprisingly, the experimental results further demonstrate that the resulting drawings produced by our method have fewer edge crossings, a shorter total edge length and more uniform edge lengths, compared to classical spring algorithms.

Graphs are often used to encapsulate relationship between objects. Graph drawing enables visualization of such relationships. The usefulness of this visual representation is dependent on whether the drawing is aesthetic. While there are no strict criteria for aesthetics of a drawing, it is generally agreed, for example,

Abstract — Numerous migration datasets become available nowadays, but appropriate techniques and tools for their exploration still have to be developed. The most widely used representations of migrations are flow maps: they are familiar to the most people and are therefore “naturally ” readable. However, they do not scale well due to the cluttering which increases with the number of represented flows. Many different techniques attempting to solve the cluttering problem have been developed, most of which involve either summarization or information extraction. Despite being effective in finding answers to certain questions concerning the data, all of these techniques have their shortcomings and there is no clear winner among them. A thorough evaluation must be performed to reveal which techniques are superior in helping the analysts with their tasks in the exploration of migration data. Another important issue, which also has to be addressed, is facilitating the exploration of temporal changes in migration flows, as many of the datasets have a temporal dimension. There are many techniques for analyzing spatio-temporal data, but only a handful of them can be applied directly to migration flows: e.g. animation or “small multiples”. However, these “simple ” techniques prove to be only of limited use, and therefore more appropriate solutions have to be developed. A challenging task is to find an effective representation of migrations which would show the temporal development and retain the spatial properties at the same time. In this paper we present the PhD thesis in which we plan to address these issues and develop, implement and evaluate visual analytics techniques and tools which can facilitate the exploration of migration flows and their development in time. Index Terms—Migration flows, spatio-temporal visualization, flow maps, thematic maps. 1

In this poster we present visualizations of the UNHCR Refugee Dataset produced with JFlowMap, a graphical tool for the visualization and exploration of spatial interactions and their development in time. The basic view provided by the tool is the flow map which represents entities flowing between geographical locations with lines connecting the flow sources and the destinations. We show example visualizations produced by our tool, briefly discuss the tool’s support for interaction and the advanced techniques for reducing the cluttering of the flow map visualizations: namely, flow aggregation and bundling. Finally, we discuss the analysis of the changes over time in the dataset and the future research directions.

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Graph visualization has been widely used to understand and present both global structural and local adjacency information in relational datasets (e.g., transportation networks, citation networks, or social networks). Graphs with dense edges, however, are difficult to visualize because fast layout and good clarity are not always easily achieved. When the number of edges is large, edge bundling can be used to improve the clarity, but in many cases, the edges could be still too cluttered to permit correct interpretation of the relations between nodes. In this paper, we present an ambiguity-free edge-bundling method especially for improving local detailed view of a complex graph. Our method makes more efficient use of display space and supports detail-on-demand viewing through an interactive interface. We demonstrate the effectiveness of our method with a public coauthorship network data.

The use of network centralities in the field of network analysis plays an important role when the relative importance of nodes within the network topology should be rated. A single network can easily be represented by the use of standard graph drawing algorithms, but not only the exploration of one centrality might be important: the comparison of two or more of them is often crucial for a better understanding. When visualizing the comparison of several network centralities, we are facing new problems of how to show them in a meaningful way. For instance, we want to be able to track all the changes of centralities in the networks as well as to display the single networks as best as possible. In the life sciences, centrality measures help scientists to understand the underlying biological processes and have been successfully applied to different biological networks. The aim of this paper is to present a novel system for the interactive visualization of biochemical networks and its centralities. Researchers can focus on the exploration of the centrality values including the network structure without dealing with visual clutter or occlusions of nodes. Simultaneously, filtering based on statistical data concerning the network elements and centrality values supports this. 1

Abstract—Flow maps are thematic maps that visualize the movement of objects, such as people or goods, between geographic regions. One or more sources are connected to several targets by lines whose thickness corresponds to the amount of flow between a source and a target. Good flow maps reduce visual clutter by merging (bundling) lines smoothly and by avoiding self-intersections. Most flow maps are still drawn by hand and only few automated methods exist. Some of the known algorithms do not support edgebundling and those that do, cannot guarantee crossing-free flows. We present a new algorithmic method that uses edge-bundling and computes crossing-free flows of high visual quality. Our method is based on so-called spiral trees, a novel type of Steiner tree which uses logarithmic spirals. Spiral trees naturally induce a clustering on the targets and smoothly bundle lines. Our flows can also avoid obstacles, such as map features, region outlines, or even the targets. We demonstrate our approach with extensive experiments. Index Terms—Flow maps, Automated Cartography, Spiral Trees. 1