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Table 3 Matrics with weak positive correlations

From: Inferring network properties based on the epidemic prevalence

\( \rho \left (\mathcal {D}_{p}(y_{0}),\mathcal {D}_{G}(\text {metric})\right) \) \( \mathcal {D}_{G}(d_{\text {max}}) \) \( \mathcal {D}_{G}(C_{G}) \) \( \mathcal {D}_{G}(\mu _{N-1}) \) \( \mathcal {D}_{G}(\rho _{D}) \) \( \mathcal {D}_{G}(N) \)
ER graphs, \( \mathcal {D}_{p}(y_{0}=0.2) \) 0.821 0.477 0.490 −0.014 −0.059
WS graphs, \( \mathcal {D}_{p}(y_{0}=0.2) \) 0.805 −0.036 −0.002 0.624 −0.012
BA graphs, \( \mathcal {D}_{p}(y_{0}=0.2) \) 0.386 0.358 0.854 0.595 −0.031
SF graphs, \( \mathcal {D}_{p}(y_{0}=0.2) \) 0.398 0.182 0.657 0.013 −0.038
ER graphs, \( \mathcal {D}_{p}(y_{0}=1.0) \) 0.856 0.525 0.524 0.082 −0.018
WS graphs, \( \mathcal {D}_{p}(y_{0}=1.0) \) 0.807 −0.031 0.081 0.666 −0.039
BA graphs, \( \mathcal {D}_{p}(y_{0}=1.0) \) 0.284 0.410 0.813 0.535 −0.003
SF graphs, \( \mathcal {D}_{p}(y_{0}=1.0) \) 0.247 0.100 0.659 0.006 0.034