|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM166361844 |
| 003 |
DE-627 |
| 005 |
20231223110637.0 |
| 007 |
tu |
| 008 |
231223s2006 xx ||||| 00| ||eng c |
| 028 |
5 |
2 |
|a pubmed24n0555.xml
|
| 035 |
|
|
|a (DE-627)NLM166361844
|
| 035 |
|
|
|a (NLM)17080803
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Kumar, Gautam
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Visual exploration of complex time-varying graphs
|
| 264 |
|
1 |
|c 2006
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
|
| 338 |
|
|
|a Band
|b nc
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 12.01.2007
|
| 500 |
|
|
|a Date Revised 03.11.2006
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Many graph drawing and visualization algorithms, such as force-directed layout and line-dot rendering, work very well on relatively small and sparse graphs. However, they often produce extremely tangled results and exhibit impractical running times for highly non-planar graphs with large edge density. And very few graph layout algorithms support dynamic time-varying graphs; applying them independently to each frame produces distracting temporally incoherent visualizations. We have developed a new visualization technique based on a novel approach to hierarchically structuring dense graphs via stratification. Using this structure, we formulate a hierarchical force-directed layout algorithm that is both efficient and produces quality graph layouts. The stratification of the graph also allows us to present views of the data that abstract away many small details of its structure. Rather than displaying all edges and nodes at once, resulting in a convoluted rendering, we present an interactive tool that filters edges and nodes using the graph hierarchy and allows users to drill down into the graph for details. Our layout algorithm also accommodates time-varying graphs in a natural way, producing a temporally coherent animation that can be used to analyze and extract trends from dynamic graph data. For example, we demonstrate the use of our method to explore financial correlation data for the U.S. stock market in the period from 1990 to 2005. The user can easily analyze the time-varying correlation graph of the market, uncovering information such as market sector trends, representative stocks for portfolio construction, and the interrelationship of stocks over time
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Garland, Michael
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 12(2006), 5 vom: 11. Sept., Seite 805-12
|w (DE-627)NLM098269445
|x 1941-0506
|7 nnns
|
| 773 |
1 |
8 |
|g volume:12
|g year:2006
|g number:5
|g day:11
|g month:09
|g pages:805-12
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 12
|j 2006
|e 5
|b 11
|c 09
|h 805-12
|