The visualization of dynamicgraphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying *** sparse and small graphs,the most efficient approach to su...
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The visualization of dynamicgraphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying *** sparse and small graphs,the most efficient approach to such visualization is node-link diagrams,whereas for dense graphs with attached data,adjacency matrices might be the better *** graphs can contain both properties,being globally sparse and locally dense,a combination of several visual metaphors as well as static and dynamicvisualizations is *** this paper,a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is *** the novelty of this technique,insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other ***,the importance of nodes and node groups can be detected,computed,and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of *** an additional feature set,an automatic identification of groups,clusters,and outliers is provided over time,and based on the visual outcome of the node-link and matrix visualizations,the repertoire of the supported layout and matrix reordering techniques is extended,and more interaction techniques are provided when considering the dynamics of the graph ***,a small user experiment was conducted to investigate the usability of the proposed *** usefulness of the proposed tool is illustrated by applying it to a graph dataset,such as e co-authorships,co-citations,and a Comprehensible Perl Archive Network distribution.
Visualizing dynamicgraphs is challenging due to the many data dimensions to be displayed such as graph vertices and edges with their attached weights or attributes and the additional time dimension. Moreover, edge di...
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Visualizing dynamicgraphs is challenging due to the many data dimensions to be displayed such as graph vertices and edges with their attached weights or attributes and the additional time dimension. Moreover, edge directions with multiplicities and the graph topology are also important inherent features. However, in many dynamic graph visualization techniques each graph in a sequence is treated the same way, i.e., it is visually encoded in the same visual metaphor or even in the same layout. This visualization strategy can be problematic if the graphs are changing topologically over time, i.e., if a sparse graph becomes denser and denser over time or a star pattern is changing into a dense cluster of connected vertices. Such a dynamicgraph data scenario demands for a visualization approach which is able to adapt the applied visual metaphor to each graph separately. In this paper we describe the dynamicgraph wall to solve this problem by using multiple visual metaphors for dynamicgraphs which are computed automatically by algorithms analysing each individual graph based on a given repertoire of graph features. The biggest issue in this technique for the graphdynamics, however, is the preservation of the viewer's mental map at metaphor changes, i.e., to guide him through the graph changes with the goal to explore the data for time-varying patterns. To reach this goal we support the analyst by an interactive highlighting feature but we also display graphs in comparative metaphor rows to visually investigate the commonalities and differences over time.
We introduce a novel technique for visualizing dense time-varying directed and weighted multi-graphs with an additional hierarchical organization of the graph nodes. Combining Indented Tree Plots and TimeRadarTrees, w...
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ISBN:
(纸本)9780769544762
We introduce a novel technique for visualizing dense time-varying directed and weighted multi-graphs with an additional hierarchical organization of the graph nodes. Combining Indented Tree Plots and TimeRadarTrees, we show the temporal evolution of relations in a static view. The graph edges are layered around thumbnail wheels consisting of color-coded sectors that are representatives of the graph nodes. These sectors generate implicit representations of graph edges. Start and target vertices are perceived by inspecting the color coding of sectors in the context of other sectors and their orientations. The technique puts emphasis on newer relations and hence, these are mapped to a larger display space in the radial diagram. The benefit of our technique is reduction of visual clutter from which node-link diagrams typically suffer. The visualization focuses on an easy exploration of trends, countertrends, periodicity, temporal shifts, and anomalies in time-varying relational data. We demonstrate the usefulness of the approach by applying it to dense dynamicgraph data acquired from a soccer match of the 2D Soccer Simulation League.
Analysis of large dynamic networks is a thriving research field, typically relying on 2D graph representations. The advent of affordable head mounted displays sparked new interest in the potential of 3D visualization ...
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ISBN:
(纸本)9781728156040
Analysis of large dynamic networks is a thriving research field, typically relying on 2D graph representations. The advent of affordable head mounted displays sparked new interest in the potential of 3D visualization for immersive network analytics. Nevertheless, most solutions do not scale well with the number of nodes and edges and rely on conventional fly- or walk-through navigation. In this paper, we present a novel approach for the exploration of large dynamicgraphs in virtual reality that interweaves two navigation metaphors: overview exploration and immersive detail analysis. We thereby use the potential of state-of-the-art VR headsets, coupled with a web-based 3D rendering engine that supports heterogeneous input modalities to enable ad-hoc immersive network analytics. We validate our approach through a performance evaluation and a case study with experts analyzing medical data.
Venture capital (VC) plays an important role in the development of Western economies, fostering innovation and renewal in the broader economy and revealing the dynamics of different frontier industries over time. Howe...
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ISBN:
(纸本)9798350322446
Venture capital (VC) plays an important role in the development of Western economies, fostering innovation and renewal in the broader economy and revealing the dynamics of different frontier industries over time. However, discovering information about industry changes through VC data has been a challenge for partners and research scholars. Researchers have applied many statistical and empirical methods to explore trends and network relationships in VC, but they are often unable to explain how entities in such networks evolve, a difficulty created by the large, heterogeneous, and dynamic nature of VC data. To help them identify industry changes, we designed InvestLens, an interactive visual analytics system to explore the VC syndication network. It identifies the overall pattern and dynamic network evolution of VC and reveals the evolution of related industries. Two case studies and interviews with domain experts validate the validity of InvestLens.
In this paper, we present a visual analysis system to explore sparse traffic trajectory data recorded by transportation cells. Such data contains the movements of nearly all moving vehicles on the major roads of a cit...
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In this paper, we present a visual analysis system to explore sparse traffic trajectory data recorded by transportation cells. Such data contains the movements of nearly all moving vehicles on the major roads of a city. Therefore it is very suitable for macro-traffic analysis. However, the vehicle movements are recorded only when they pass through the cells. The exact tracks between two consecutive cells are unknown. To deal with such uncertainties, we first design a local animation, showing the vehicle movements only in the vicinity of cells. Besides, we ignore the micro-behaviors of individual vehicles, and focus on the macro-traffic patterns. We apply existing trajectory aggregation techniques to the dataset, studying cell status pattern and inter-cell flow pattern. Beyond that, we propose to study the correlation between these two patterns with dynamic graph visualization techniques. It allows us to check how traffic congestion on one cell is correlated with traffic flows on neighbouring links, and with route selection in its neighbourhood. Case studies show the effectiveness of our system.
Large dynamic networks are targets of analysis in many fields. Tracking temporal changes at scale in these networks is challenging due in part to the fact that small changes can be missed or drowned-out by the rest of...
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Large dynamic networks are targets of analysis in many fields. Tracking temporal changes at scale in these networks is challenging due in part to the fact that small changes can be missed or drowned-out by the rest of the network. For static networks, current approaches allow the identification of specific network elements within their context. However, in the case of dynamic networks, the user is left alone with finding salient local network elements and tracking them over time. In this work, we introduce a modular DoI specification to flexibly define what salient changes are and to assign them a measure of their importance in a time-varying setting. The specification takes into account neighborhood structure information, numerical attributes of nodes/edges, and their temporal evolution. A tailored visualization of the DoI specification complements our approach. Alongside a traditional node-link view of the dynamic network, it serves as an interface for the interactive definition of a DoI function. By using it to successively refine and investigate the captured details, it supports the analysis of dynamic networks from an initial view until pinpointing a user's analysis goal. We report on applying our approach to scientific coauthorship networks and give concrete results for the DBLP data set.
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