Egocentric storylines for visual analysis of large dynamic graphs

Chris W. Muelder, Tarik Crnovrsanin, Arnaud Sallaberry, Kwan-Liu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013
Pages56-62
Number of pages7
DOIs
Publication statusPublished - Dec 1 2013
Event2013 IEEE International Conference on Big Data, Big Data 2013 - Santa Clara, CA, United States
Duration: Oct 6 2013Oct 9 2013

Other

Other2013 IEEE International Conference on Big Data, Big Data 2013
CountryUnited States
CitySanta Clara, CA
Period10/6/1310/9/13

Keywords

  • dynamic graphs
  • egocentric views
  • information visualization
  • storylines

ASJC Scopus subject areas

  • Software

Cite this

Muelder, C. W., Crnovrsanin, T., Sallaberry, A., & Ma, K-L. (2013). Egocentric storylines for visual analysis of large dynamic graphs. In Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013 (pp. 56-62). [6691715] https://doi.org/10.1109/BigData.2013.6691715