Table 5.

The optimal $SI$-value of different brain network models for networks modeling in the stage from NC to AD. ${\xi }_C$ represents the relative error in clustering coefficient between synthetic networks and the real target brain network (TN); ${\xi }_{Eloc}$ represents the relative error in local efficiency; ${\xi }_M$ is the relative error in modularity; ${\xi }_L$ is the relative error in the characteristic path length; ${\xi }_{Eglob}$ is the relative error in global efficiency; and ${\xi }_T$ is the relative error in transitivity.

Models	$\mathit{\lambda }$	$\mathit{\eta }$	${\mathit{\xi }}_{\mathit{C}}$	${\mathit{\xi }}_{\mathit{Eloc}}$	${\mathit{\xi }}_{\mathit{M}}$	${\mathit{\xi }}_{\mathit{L}}$	${\mathit{\xi }}_{\mathit{Eglob}}$	${\mathit{\xi }}_{\mathit{T}}$	SI
CN	0.4	1.2	0.0426	0.0491	0.1263	0.0665	0.1460	0.0419	2.1169
PA	0.2	1.6	0.0194	0.0289	0.0395	0.0551	0.1377	0.0515	3.0111
AA	0.2	1.6	0.0386	0.0339	0.1291	0.0576	0.0551	0.0881	2.4851
RA	0.2	2.0	0.0448	0.0245	0.1370	0.0732	0.0496	0.0758	2.4697
JC	0.4	0.4	0.0653	0.0661	0.1249	0.0364	0.0786	0.0709	2.2614
MINM	0.2	1.8	0.0432	0.0358	0.0835	0.0253	0.0303	0.0368	3.9231
Random	–	–	0.0888	0.1355	0.2304	0.0534	0.0611	0.1476	1.3951