可视化来自社交媒体流的图形差异

Minjeong Shin, Dongwoo Kim, Jae Hee Lee, Umanga Bista, Lexing Xie
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引用次数: 0

摘要

我们提出了KGdiff,一个新的交互式可视化工具,用于关注实体和关系的社交媒体内容。核心组件是一个布局算法,它突出显示两个图之间的差异。我们将该算法应用于从不同时间段的文本流中提取的由命名实体及其关系组成的知识图谱。可视化系统提供了额外的信息,如实体的数量和频率排名,并允许用户选择图形的哪些部分进行交互式可视化。在Twitter和新闻文章集合上,KGdiff允许用户比较不同的数据子集。这种比较的结果往往揭示了讨论中主题或地理的变化。更广泛地说,图的差异对于各种关系数据比较任务都很有用,比如比较社会交互图、识别用户行为的变化,或者发现不同来源、地理位置或政治立场的图的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visualizing Graph Differences from Social Media Streams
We propose KGdiff, a new interactive visualization tool for social media content focusing on entities and relationships. The core component is a layout algorithm that highlights the differences between two graphs. We apply this algorithm on knowledge graphs consisting of named entities and their relations extracted from text streams over different time periods. The visualization system provides additional information such as the volume and frequency ranking of entities and allows users to select which parts of the graph to visualize interactively. On Twitter and news article collections, KGdiff allows users to compare different data subsets. Results of such comparisons often reveal topical or geographical changes in a discussion. More broadly, graph differences are useful for a wide range of relational data comparison tasks, such as comparing social interaction graphs, identifying changes in user behavior, or discovering differences in graphs from distinct sources, geography, or political stance.
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