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. 2011 Oct 13;6:53. doi: 10.1186/1745-6150-6-53

Figure 4.

Figure 4

The feature space for the distance-based approach. To compare the two approaches, we visualize the feature space of the two top-ranked features by ANOVA. (a) represents feature space of the descriptor-based approach where Fi=1,2desc. (b) represents feature space of the non distance-based approach where Fi=1,2.deg. deg. To gain the features vectors in (b), we calculate the Kullback-Leibler divergence (KLD) based on the degree distribution between each network. This leads to a feature vector for each network containing the KLD for each of the other networks.