arXiv 2102.019742021-02-03cs.SIcs.HCcs.LGcs.MM

AttentionFlow：时间序列网络影响的可视化

AttentionFlow: Visualising Influence in Networks of Time Series

Australian National University（澳大利亚国立大学）；Data61, CSIRO（联邦科学与工业研究组织数据61研究所）

Minjeong Shin, Alasdair Tran, Siqi Wu, Alexander Mathews, Rong Wang, Georgiana Lyall, Lexing Xie

备注：: Published in WSDM 2021. The demo is available at https://attentionflow.ml and code is available at https://github.com/alasdairtran/attentionflow
发表信息：: The Proceedings of the Fourteenth ACM International Conference on Web Search and Data Mining (WSDM), 2021
DOI：: 10.1145/3437963.3441703

摘要：

对网页、搜索词和视频等在线项目的集体关注反映了社会、文化和经济利益的趋势。此外，不同项目的关注趋势通过超链接或推荐等机制表现出相互影响。有许多可视化工具用于时间序列、网络演化或网络影响;然而，很少有系统将这三者联系起来。在这项工作中，我们提出了AttentionFlow，这是一个新的系统，可以可视化时间序列网络及其相互之间的动态影响。围绕一个自我节点，我们的系统同时提出了每个节点上的时间序列，使用两个视觉编码：一个树环的概述和一个线图的细节。AttentionFlow支持交互，例如覆盖影响的时间序列和按时间或通量过滤邻居。我们使用两个真实世界的数据集，VevoMusic和WikiTraffic演示AttentionFlow。我们发现，歌曲中的注意力峰值可以用外部事件（如重大奖项）或网络变化（如新歌发布）来解释。单独的案例研究也展示了艺术家的影响力如何在他们的职业生涯中发生变化，以及相关的维基百科流量是由文化兴趣驱动的。更广泛地说，AttentionFlow可以概括为可视化物理基础设施（如道路网络）或自然现象（如天气和地质测量）的时间序列网络。

英文摘要：

The collective attention on online items such as web pages, search terms, and videos reflects trends that are of social, cultural, and economic interest. Moreover, attention trends of different items exhibit mutual influence via mechanisms such as hyperlinks or recommendations. Many visualisation tools exist for time series, network evolution, or network influence; however, few systems connect all three. In this work, we present AttentionFlow, a new system to visualise networks of time series and the dynamic influence they have on one another. Centred around an ego node, our system simultaneously presents the time series on each node using two visual encodings: a tree ring for an overview and a line chart for details. AttentionFlow supports interactions such as overlaying time series of influence and filtering neighbours by time or flux. We demonstrate AttentionFlow using two real-world datasets, VevoMusic and WikiTraffic. We show that attention spikes in songs can be explained by external events such as major awards, or changes in the network such as the release of a new song. Separate case studies also demonstrate how an artist's influence changes over their career, and that correlated Wikipedia traffic is driven by cultural interests. More broadly, AttentionFlow can be generalised to visualise networks of time series on physical infrastructures such as road networks, or natural phenomena such as weather and geological measurements.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。