Figure 7.

The N2.1 network layout optimized with four different methods, two different approaches were used for clustered graph visualization. In 7.a the network was optimized with the FragViz algorithm. For 7.b a complete weighted graph was first constructed from the original network and similarity matrix. The weights of the network edges were scaled so that the largest weight equalled 1. Virtual edges were added to all unconnected pairs of vertices, with weights inversely linear with the distances from the similarity matrix and scaled to interval [0, 0.01]. The complete graph was then optimized with the FR algorithm. For 7.c the original network was merged to the dissimilarity matrix, where pairs of connected vertices from the original network had the lowest value in the similarity network 0, while other values from the dissimilarity matrix were 100 times smaller [0.99, 1]. The dissimilarity matrix was than optimized with the MDS algorithm. In 7.d and 7.e we optimized a network using clustered graph visualization. We transformed the original graph G and dissimilarity matrix D to a clustered graph C′ = (G′, T′) in two different ways.

Štajdohar et al. BMC Bioinformatics 2010 11:475   doi:10.1186/1471-2105-11-475
Download authors' original image