Table 4.

The optimal $SI$-value of different brain network models for networks modeling in the stage from NC to MCI. ${\xi }_C$ is the relative error of clustering coefficient between synthetic networks and the real target brain network (TN); ${\xi }_{Eloc}$ and ${\xi }_{Eglob}$ represent the relative errors of local efficiency and global efficiency; and ${\xi }_M$, ${\xi }_L$, and ${\xi }_T$ are the relative errors of modularity, the characteristic path length, and transitivity, respectively. A larger value of $SI$ indicates that the model could generate synthetic networks with properties more similar to the real target brain network of MCI.

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
ECM	0.2	1.6	0.0687	0.0302	0.0713	0.1851	0.1413	0.0205	1.9339
PA	0.2	1.8	0.0213	0.0187	0.0639	0.1464	0.1356	0.0480	2.4010
AA	0.2	1.4	0.0811	0.0578	0.1210	0.1324	0.0514	0.0173	2.1692
RA	0.4	2.0	0.0975	0.0638	0.0465	0.1345	0.0502	0.0164	2.4358
JC	0.2	0.2	0.0844	0.0515	0.1082	0.0861	0.0789	0.0126	2.3714
MINM	0.4	2.0	0.0816	0.0077	0.0727	0.0598	0.0292	0.0010	3.9683
Random	–	–	0.1406	0.1132	0.1796	0.1558	0.1204	0.2068	1.0912