Egocentric storylines for visual analysis of large dynamic graphs

Abstract : Large dynamic graphs occur in many fields. While overviews are often used to provide summaries of the overall structure of the graph, they become less useful as data size increases. Often analysts want to focus on a specific part of the data according to domain knowledge, which is best suited by a bottom-up approach. This paper presents an egocentric, bottom-up method to exploring a large dynamic network using a storyline representation to summarise localized behavior of the network over time.
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Chris Muelder, Tarik Crnovrsanin, Arnaud Sallaberry, Kwan-Liu Ma. Egocentric storylines for visual analysis of large dynamic graphs. Big Data: International Conference on Big Data, Oct 2013, Santa Clara, United States. pp.56-62, ⟨10.1109/BigData.2013.6691715⟩. ⟨lirmm-01275387⟩

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