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. Event-based egocentric dynamic networks are an important class of networks widely seen in many domains. In this paper, we present a visual analytics approach for these networks by combining data-driven network simplifications with a novel visualization design - EgoNetCloud. In particular, an integrated data processing pipeline is proposed to prune, compress and filter the networks into smaller but salient abstractions. To accommodate the simplified network into the visual design, we introduce a constrained graph layout algorithm on the dynamic network. Through a real-life case study as well as conversations with the domain expert, we demonstrate the effectiveness of the EgoNetCloud design and system in completing analysis tasks on event-based dynamic networks. The user study comparing EgoNetCloud with a working system on academic search confirms the effectiveness and convenience of our visual analytics based approach. The visual analysis of dynamic networks is a challenging task. In this paper, we introduce a new approach supporting the discovery of substructures sharing a similar trend over time by combining computation, visualization and interaction. With existing techniques, their discovery would be a tedious endeavor because of the number of nodes, edges as well as time points to be compared. First, on the basis of the supergraph, we therefore group nodes and edges according to their associated attributes that are changing over time. Second, the supergraph is visualized to provide an overview of the groups of nodes and edges with similar behavior over time in terms of their associated attributes. Third, we provide specific interactions to explore and refine the temporal clustering, allowing the user to further steer the analysis of the dynamic network. We demonstrate our approach by the visual analysis of a large wireless mesh network. Visualization plays a crucial role in understanding dynamic social networks at many different levels (i.e., group, subgroup, and individual). Node-link-based visualization techniques are currently widely used for these tasks and have been demonstrated to be effective, but it was found that they also have limitations in representing temporal changes, particularly at the individual and subgroup levels. To overcome these limitations, this article presents a new network visualization technique, called “TimeMatrix,” based on a matrix representation. Interaction techniques, such as overlay controls, a temporal range slider, semantic zooming, and integrated network statistical measures, support analysts in studying temporal social networks. To validate the design, the article presents a user study involving three social scientists analyzing inter-organizational collaboration data. The study demonstrates how TimeMatrix may help analysts gain insights about the temporal aspects of network data that can be subseque...