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. 2019 Oct 22;9:15095. doi: 10.1038/s41598-019-51209-6

Table 2.

Hybrid methods for the identification of influential spreaders in networks.

Method Features Subcrit. Critical Supercrit.
AD cAD 1.000 1.000 1.000
gm 0.993 0.961 0.931
rm 0.755 0.548 0.119
CD cCD 1.000 1.000 1.000
gm 0.983 0.963 0.929
rm 0.730 0.525 0.100
B cB 1.000 1.000 1.000
gm 0.946 0.954 0.938
rm 0.590 0.483 0.110
AD,B cAD 0.718 0.590 0.023
cB −0.027 0.046 0.069
gm 0.987 0.964 0.936
rm 0.755 0.551 0.116
AD,PR,LR cAD 1.189 1.044 0.115
cPR −0.266 0.145 0.772
cLR −0.336 −0.632 −0.771
gm 0.991 0.980 0.971
rm 0.806 0.616 0.300
PR,LR,CD cPR 0.006 0.386 0.803
cLR −0.419 −0.702 −0.771
cCD 1.028 0.898 0.088
gm 0.985 0.979 0.971
rm 0.784 0.597 0.293
AD,B,LR cAD 1.096 1.047 0.343
cB −0.010 0.067 0.083
cLR −0.466 −0.565 −0.395
gm 0.993 0.976 0.952
rm 0.810 0.625 0.220
PR,LR,EI cPR 0.304 0.583 0.740
cLR 0.101 −0.251 −0.733
cEI 0.235 0.277 0.121
gm 0.973 0.964 0.970
rm 0.698 0.589 0.304

The table is organized in various blocks, each corresponding to a specific method. For every method m, either individual or hybrid, we report performance values for the three different dynamical regimes in terms of overall performance gm and overall precision rm. The top three blocks correspond to the best individual methods in the three regimes according to overall performance metric. The remaining blocks are for hybrid methods. In each block, the first rows report values of the coefficient cm of the individual method m in the definition of the hybrid method. We report the averages for the coefficient values over 1,000 iterations of the learning algorithm. The bottom two rows in each block correspond instead to the values of the performance metrics. Errors associated with all these measures are always smaller than 0.001, and they are omitted from the table for clarity.