TY - JOUR
T1 - Volume-based large dynamic graph analysis supported by evolution provenance
AU - Bruder, Valentin
AU - Ben Lahmar, Houssem
AU - Hlawatsch, Marcel
AU - Frey, Steffen
AU - Burch, Michael
AU - Weiskopf, Daniel
AU - Herschel, Melanie
AU - Ertl, Thomas
PY - 2019/12/1
Y1 - 2019/12/1
N2 - We present an approach for the visualization and interactive analysis of dynamic graphs that contain a large number of time steps. A specific focus is put on the support of analyzing temporal aspects in the data. Central to our approach is a static, volumetric representation of the dynamic graph based on the concept of space-time cubes that we create by stacking the adjacency matrices of all time steps. The use of GPU-accelerated volume rendering techniques allows us to render this representation interactively. We identified four classes of analytics methods as being important for the analysis of large and complex graph data, which we discuss in detail: data views, aggregation and filtering, comparison, and evolution provenance. Implementations of the respective methods are presented in an integrated application, enabling interactive exploration and analysis of large graphs. We demonstrate the applicability, usefulness, and scalability of our approach by presenting two examples for analyzing dynamic graphs. Furthermore, we let visualization experts evaluate our analytics approach.
AB - We present an approach for the visualization and interactive analysis of dynamic graphs that contain a large number of time steps. A specific focus is put on the support of analyzing temporal aspects in the data. Central to our approach is a static, volumetric representation of the dynamic graph based on the concept of space-time cubes that we create by stacking the adjacency matrices of all time steps. The use of GPU-accelerated volume rendering techniques allows us to render this representation interactively. We identified four classes of analytics methods as being important for the analysis of large and complex graph data, which we discuss in detail: data views, aggregation and filtering, comparison, and evolution provenance. Implementations of the respective methods are presented in an integrated application, enabling interactive exploration and analysis of large graphs. We demonstrate the applicability, usefulness, and scalability of our approach by presenting two examples for analyzing dynamic graphs. Furthermore, we let visualization experts evaluate our analytics approach.
KW - Dynamic graphs
KW - Evolution provenance
KW - Visual analytics
KW - Volume rendering
UR - http://www.scopus.com/inward/record.url?scp=85068347984&partnerID=8YFLogxK
U2 - 10.1007/s11042-019-07878-6
DO - 10.1007/s11042-019-07878-6
M3 - Article
AN - SCOPUS:85068347984
VL - 78
SP - 32939
EP - 32965
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
SN - 1380-7501
IS - 23
ER -