Research into social network analysis has shown that graph metrics, such as degree and closeness, are often used to summarize structural changes in a dynamic graph. However there have been few visual analytics approaches that have been proposed to help analysts study graph evolutions in the context of graph metrics. In this paper, we present a novel approach, called GraphFlow, to visualize dynamic graphs. In contrast to previous approaches that provide users with an animated visualization, GraphFlow offers a static flow visualization that summarizes the graph metrics of the entire graph and its evolution over time. Our solution supports the discovery of high-level patterns that are difficult to identify in an animation or in individual static representations. In addition, GraphFlow provides users with a set of interactions to create filtered views. These views allow users to investigate why a particular pattern has occurred. We showcase the versatility of GraphFlow using two different datasets and describe how it can help users gain insights into complex dynamic graphs.<\/p>\n","protected":false},"excerpt":{"rendered":"
Research into social network analysis has shown that graph metrics, such as degree and closeness, are often used to summarize structural changes in a dynamic graph. However there have been few visual analytics approaches that have been proposed to help analysts study graph evolutions in the context of graph metrics. 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