Egocentric storylines for visual analysis of large dynamic graphs

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

doi:10.1109/BigData.2013.6691715

Egocentric storylines for visual analysis of large dynamic graphs

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

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013
Pages	56-62
Number of pages	7
DOIs	https://doi.org/10.1109/BigData.2013.6691715
State	Published - Dec 1 2013
Event	2013 IEEE International Conference on Big Data, Big Data 2013 - Santa Clara, CA, United States Duration: Oct 6 2013 → Oct 9 2013

Other

Other	2013 IEEE International Conference on Big Data, Big Data 2013
Country/Territory	United States
City	Santa Clara, CA
Period	10/6/13 → 10/9/13

Keywords

dynamic graphs
egocentric views
information visualization
storylines

ASJC Scopus subject areas

Software

Access to Document

10.1109/BigData.2013.6691715

Cite this

Muelder, CW, 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., 6691715, pp. 56-62, 2013 IEEE International Conference on Big Data, Big Data 2013, Santa Clara, CA, United States, 10/6/13. https://doi.org/10.1109/BigData.2013.6691715

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N2 - 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.

AB - 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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KW - storylines

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M3 - Conference contribution

AN - SCOPUS:84893282263

SN - 9781479912926

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BT - Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013

T2 - 2013 IEEE International Conference on Big Data, Big Data 2013

Y2 - 6 October 2013 through 9 October 2013

ER -

Egocentric storylines for visual analysis of large dynamic graphs

Abstract

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Keywords

ASJC Scopus subject areas

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