TY - GEN
T1 - Temporal multivariate networks
AU - Archambault, Daniel
AU - Abello, James
AU - Kennedy, Jessie
AU - Kobourov, Stephen
AU - Ma, Kwan Liu
AU - Miksch, Silvia
AU - Muelder, Chris
AU - Telea, Alexandru C.
PY - 2014
Y1 - 2014
N2 - In previous chapters, this book has primarily concerned itself with visualization methods for static, multivariate graphs. In a static scenario, the network has a number of attributes associated with its elements. These attribute values remain fixed and the challenge is to visualize the interactions between the network(s) and these attributes. Static multivariate graphs could be viewed as graphs with an associated high dimensional data set linked to its elements. Time is simply another dimension in this multivariate data set that can interact with the vertices, edges, and attribute values of the network. However, humans perceive time differently as we know from our everyday interactions with the physical world. Thus, intuitively, this dimension is often handled differently when supporting the presentation of data that changes over time. Visualization applications and techniques have, and probably should, continue to exploit this fact, allowing for effective visualization methods of temporal multivariate graphs.
AB - In previous chapters, this book has primarily concerned itself with visualization methods for static, multivariate graphs. In a static scenario, the network has a number of attributes associated with its elements. These attribute values remain fixed and the challenge is to visualize the interactions between the network(s) and these attributes. Static multivariate graphs could be viewed as graphs with an associated high dimensional data set linked to its elements. Time is simply another dimension in this multivariate data set that can interact with the vertices, edges, and attribute values of the network. However, humans perceive time differently as we know from our everyday interactions with the physical world. Thus, intuitively, this dimension is often handled differently when supporting the presentation of data that changes over time. Visualization applications and techniques have, and probably should, continue to exploit this fact, allowing for effective visualization methods of temporal multivariate graphs.
UR - http://www.scopus.com/inward/record.url?scp=84901261125&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84901261125&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-06793-3_8
DO - 10.1007/978-3-319-06793-3_8
M3 - Conference contribution
AN - SCOPUS:84901261125
SN - 9783319067926
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 151
EP - 174
BT - Multivariate Network Visualization - Dagstuhl Seminar #13201, Revised Discussions
PB - Springer-Verlag
T2 - 3rd Dagstuhl Seminar on Information Visualization - Towards Multivariate Network Visualization
Y2 - 12 May 2013 through 17 May 2013
ER -