Adaptive Disentanglement Based on Local Clustering in Small-World Network Visualization

Small-world networks have characteristically low pairwise shortest-path distances, causing distance-based layout methods to generate hairball drawings. Recent approaches thus aim at finding a sparser representation of the graph to amplify variations in pairwise distances. Since the effect of sparsification on the layout is difficult to describe analytically, the incorporated filtering parameters of these approaches typically have to be selected manually and individually for each input instance. We here propose the use of graph invariants to determine suitable parameters automatically. This allows us to perform adaptive filtering to obtain drawings in which the cluster structure is most prominent. The approach is based on an empirical relationship between input and output characteristics that is derived from real and synthetic networks. Experimental evaluation shows the effectiveness of our approach and suggests that it can be used by default to increase the robustness of force-directed layout methods.