Smooth animation of structure evolution in time-varying graphs with pattern matching

Abstract

Drawing a large graph into the limited display space often raises visual clutter and overlapping problems. The complex structure hinders the exploration of significant patterns of connections. For time-varying graphs, it is difficult to reveal the evolution of structures. In this paper, we group nodes and links into partitions, where objects within a partition are more closely related. Besides, partitions maintain stable across time steps. The complex structure of a partition is simplified by mapping to a pattern and the evolution is exposed by comparing patterns of two consecutive time steps. We created various visual designs to present different scenarios of changes. In order to achieve a smooth animation of time-varying graphs, we extract the graph layout at each time step from a super-layout which is based on the super-graph and super-community. The effectiveness of our approach is verified with two datasets, one is a synthetic dataset, and the other is the DBLP dataset.