A Meta Heuristic for Graph Drawing: Learning the Optimal Graph-Drawing Method for Clustered Graphs

摘要

The problem of finding a pleasant layout for a given graph is a key challenge in the field of information visualization. For graphs that are biased towards a particular property such as tree-like, star-like, or bipartite, a layout algorithm can produce excellent layouts―if this property is actually detected. Typically, a graph may not be of such a homogeneous shape but is comprised of different parts, or it provides several levels of abstraction each of which dominated by another property. The paper in hand addresses the layout of such graphs. It presents a meta heuristic for graph drawing, which is based on two ideas: (ⅰ) The detection and exploitation of hierarchical cluster information to unveil a graph's inherent structure, (ⅱ) The automatic selection of an individual graph drawing method for each cluster.