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Fig. 8 | Applied Network Science

Fig. 8

From: Computational intractability law molds the topology of biological networks

Fig. 8

Computational intractability as a predictive tool of degree distribution. a The percentage of nodes having a degree d in Fly PPI network; the fraction of degree-d nodes is inversely proportional to the potential optimization ambiguity that a degree-d node adds to instances of NEP (see text). b Accuracy of predicting the degree distribution of PPI (top), regulatory (middle) and DB-sourced (bottom) networks (degree distribution plots of all networks is included in SI 9). Accuracy = 100 - \(\sum |predicted(d)-actual(d)|\) over each degree d in the network (predicted(d)=E(d) (see text) and actual(d)=the fraction of genes having degree d). cα,β to edge:node (e2n) and node:edge (n2e) ratios (n2e=e2n−1), respectively, in the prediction formula E(d). α and β values were numerically optimized and emerged to be proportional to n2e and e2n values, respectively. The average ±SD of (α vs. n2e), (β vs. e2n) are (0.43 ±0.063 vs. 0.526 ±0.133), (1.96 ±0.324 vs. 2.06 ±0.634) respectively

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