Table 5.

Comparing the distance between nodes in the optimal input set and the input set including only high-degree nodes.

Network	LCC = 2				LCC = 1
	$d_{\text{i}}(2)$	std	$d_{\text{i}}^{\text{hubs}}(2)$	std	$d_{\text{i}}(1)$	std	$d_{\text{i}}^{\text{hubs}}(1)$	std
1	3	0	1	0	1.8	.2	1.3	.1
2	2	0	1	0	2.5	.4	1.7	.1
3	2	0	1	0	2.1	.3	1.3	.2
4	3	0	1	0	2.3	.3	1.5	.1
5	3	0	1	0	2.1	.2	1.1	.0
6	3	0	1	0	2.3	.4	1.4	.1
7	3	0	1	0	2.1	.3	1.5	.1

The column labeled $d_i(\ell )$ displays the average distance between nodes in the optimal input set. This input set is the smallest possible set that can control the brain network with a LCC = $\ell$. On the other hand, $d_i^{\text{hubs}}(\ell )$ represents the average distance between nodes in the input set that has the same size as the optimal input set but includes only high-degree nodes. Additionally, the “std” column indicates the standard deviation of the mean distances.