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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