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Table 2 Matrics with strong positive correlations

From: Inferring network properties based on the epidemic prevalence

\( \rho \left (\mathcal {D}_{p}(y_{0}),\mathcal {D}_{G}(\text {metric})\right) \) \( \mathcal {D}_{G}(E[D]) \) \( \mathcal {D}_{G}(E[D^{2}]) \) \( \mathcal {D}_{G}(\lambda _{1}) \) \( \mathcal {D}_{G}(E[H]) \) \( \mathcal {D}_{G}\left (E\left [\frac {1}{H}\right ]\right) \)
ER graphs, \( \mathcal {D}_{p}(y_{0}=0.2) \) 0.941 0.856 0.940 0.953 0.939
WS graphs, \( \mathcal {D}_{p}(y_{0}=0.2) \) 0.877 0.826 0.921 0.952 0.958
BA graphs, \( \mathcal {D}_{p}(y_{0}=0.2) \) 0.940 0.838 0.871 0.952 0.945
SF graphs, \( \mathcal {D}_{p}(y_{0}=0.2) \) 0.944 0.612 0.561 0.861 0.823
ER graphs, \( \mathcal {D}_{p}(y_{0}=1.0) \) 0.947 0.866 0.944 0.948 0.932
WS graphs, \( \mathcal {D}_{p}(y_{0}=1.0) \) 0.905 0.818 0.927 0.952 0.954
BA graphs, \( \mathcal {D}_{p}(y_{0}=1.0) \) 0.945 0.856 0.908 0.954 0.948
SF graphs, \( \mathcal {D}_{p}(y_{0}=1.0) \) 0.948 0.631 0.459 0.792 0.783