Abstract
The community structure of graphs is an important feature that gives insight into the high-level organization of objects within the graph. In real-world systems, the graph topology is oftentimes not static but changes over time and hence, also the community structure changes. Previous timeline-based approaches either visualize the dynamic graph or the dynamic community structure. In contrast, our approach combines both in a single image and therefore allows users to investigate the community structure together with the underlying dynamic graph. Our optimized ordering of vertices and selection of colours in combination with interactive highlighting techniques increases the traceability of communities along the time axis. Users can identify visual signatures, estimate the reliability of the derived community structure and investigate whether community evolution interacts with changes in the graph topology. The utility of our approach is demonstrated in two application examples.
The community structure of graphs is an important feature that gives insight into the high-level organization of objects within the graph. In real-world systems, the graph topology is oftentimes not static but changes over time and hence, also the community structure changes. Previous timeline-based approaches either visualize the dynamic graph or the dynamic community structure. In contrast, our approach combines both in a single image and therefore allows users to investigate the community structure together with the underlying dynamic graph. Our optimized ordering of vertices and selection of colours in combination with interactive highlighting techniques increases the traceability of communities along the time axis. Users can identify visual signatures, estimate the reliability of the derived community structure and investigate whether community evolution interacts with changes in the graph topology. The utility of our approach is demonstrated in two application examples.