A meta heuristic for graph drawing. learning the optimal graph-drawing method for clustered graphs

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 abstrac-tion 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 informa-tion to unveil a graph’s inherent structure. (ii) The automatic selec-tion of an individual graph drawing method for each cluster