Abstract : Graphs change over time, and typically variations on the small multiples or animation pattern is used to convey this dynamism visually. However, both of these classical techniques have significant drawbacks, so a new approach, Storyline Visualization of Events on a Network (SVEN) is proposed. SVEN builds on storyline techniques, conveying nodes as contiguous lines over time. SVEN encodes time in a natural manner, along the horizontal axis, and optimizes the vertical placement of storylines to decrease clutter (line crossings, straightness, and bends) in the drawing. This paper demonstrates SVEN on several different flavors of real-world dynamic data, and outlines the remaining near-term future work.
Network analysts can often still gain insight when provided only the ego networks (the subgraph containing the ego and its direct neighbors) of individuals instead of the entire network. Based on this, developed a 1.5-dimensional'' dynamic network visualization capable of showing the ego network of a particular individual of interest over time in a single picture @cite_46 . adapted the classical parallel coordinates visualization for dynamic networks @cite_23 . In their system, vertices are drawn on parallel axes, the area between each axis represents a time interval, and edges connect vertices in adjacent axes. The inevitable problem of having an overwhelming number of edge crossings for larger datasets is addressed by reducing the opacity of the lines drawn.
@cite_46 @cite_23
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The dynamic network visualization has been a challenging topic due to the complexity introduced by the extra time dimension. Existing solutions to this problem are usually good for the overview and presentation, but not for the interactive analysis. We propose in this paper a new approach which only considers the dynamic network central to a focus node (aka dynamic ego network). The navigation of the entire network is achieved by switching the focus node with user interactions. With this approach, the complexity of the compressed dynamic network is greatly reduced without sacrificing the network and time affinity central to the focus node. As a result, we are able to present each dynamic ego network in a single static view, well supporting user analysis on temporal network patterns. We describe our general framework including the network data pre-processing, 1.5D network and trend visualization design, layout algorithms, as well as several customized interactions. In addition, we show that our approach can also be extended to visualize the event-based and multimodal dynamic networks. Finally, we demonstrate, through two practical case studies, the effectiveness of our solution in support of visual evidence and pattern discovery. We present a novel dynamic graph visualization technique based on node-link diagrams. The graphs are drawn side-byside from left to right as a sequence of narrow stripes that are placed perpendicular to the horizontal time line. The hierarchically organized vertices of the graphs are arranged on vertical, parallel lines that bound the stripes; directed edges connect these vertices from left to right. To address massive overplotting of edges in huge graphs, we employ a splatting approach that transforms the edges to a pixel-based scalar field. This field represents the edge densities in a scalable way and is depicted by non-linear color mapping. The visualization method is complemented by interaction techniques that support data exploration by aggregation, filtering, brushing, and selective data zooming. Furthermore, we formalize graph patterns so that they can be interactively highlighted on demand. A case study on software releases explores the evolution of call graphs extracted from the JUnit open source software project. In a second application, we demonstrate the scalability of our approach by applying it to a bibliography dataset containing more than 1.5 million paper titles from 60 years of research history producing a vast amount of relations between title words.