LinkWave:动态加权网络的可视化邻接表

N. Riche, Yann Riche, Nicolas Roussel, M. Carpendale, T. Madhyastha, T. Grabowski
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引用次数: 6

摘要

随着图在社会科学、工程和生物学等众多领域的性质和类型的不断增加,普通的图技术不再总是足够了。特别是,我们解决了可视化动态加权图的问题,即边缘的权重随时间变化的图,以提取连通性和排序模式。我们提出LinkWave,一种采用视觉边缘列表概念的新技术。为了更好地支持对边的权重变化的视觉探索,并表征它们的节奏模式,LinkWave将每条边表示为一个单独的时间序列,并提供一组交互来缩放、过滤、排序和聚合边。我们与神经科学家合作设计了LinkWave,旨在从功能性脑连接数据中提取由退行性疾病引起的模式。我们报告神经科学家在LinkWave中发现的初步发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LinkWave: a visual adjacency list for dynamic weighted networks
As the nature and types of graphs in numerous fields such as social sciences, engineering, and biology continue to proliferate, common graph techniques no longer always suffice. In particular, we tackle the problem of visualizing dynamic weighted graphs-graphs with edges whose weight changes over time-to extract connectivity and sequencing patterns. We present LinkWave, a novel technique employing the concept of a visual list of edges. To better support the visual exploration of weight changes in edges and to characterize their rhythmic patterns, LinkWave represents each edge as an individual time series and provides a set of interactions to zoom, filter, sort, and aggregate the edges. We designed LinkWave in collaboration with neuroscientists seeking to extract patterns caused by degenerative diseases in functional brain connectivity data. We report preliminary findings neuroscientists discovered with LinkWave.
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