Resolution:
## 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 |