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 | |
| 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 | 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
Cite this
Egocentric storylines for visual analysis of large dynamic graphs. / Muelder, Chris W.; Crnovrsanin, Tarik; Sallaberry, Arnaud; Ma, Kwan-Liu.
Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013. 2013. p. 56-62 6691715.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Egocentric storylines for visual analysis of large dynamic graphs
AU - Muelder, Chris W.
AU - Crnovrsanin, Tarik
AU - Sallaberry, Arnaud
AU - Ma, Kwan-Liu
PY - 2013/12/1
Y1 - 2013/12/1
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.
KW - dynamic graphs
KW - egocentric views
KW - information visualization
KW - storylines
UR - http://www.scopus.com/inward/record.url?scp=84893282263&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893282263&partnerID=8YFLogxK
U2 - 10.1109/BigData.2013.6691715
DO - 10.1109/BigData.2013.6691715
M3 - Conference contribution
SN - 9781479912926
SP - 56
EP - 62
BT - Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013
ER -