Visualizing graph differences from social media streams
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Shin, Minjeong
Kim, Dongwoo
Lee, Jae
Xie, Lexing
Bista, Umanga
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Association for Computing Machinery (ACM)
Abstract
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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WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining
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Restricted until
2099-12-31