Table 2.

Largest eigenvalues (expressed as mean and standard deviation), calculated for each graph model and centrality measure (CM), of the original graph (Original), the graph randomly deprived of k nodes (Random), and the graph deprived of the k most central nodes within the graph (Top-K). DC stands for degree centrality, BC for betweenees centrality, EC for eigenvalue centrality and CC for closeness centrality. A p-value lower than 0.05 means that the decrease in the distribution of the largest eigenvalue is significantly higher after deleting the Top-K central nodes.

Graph Model	CM	Original	Top-K	Random	p-value
Erdős Rényi	DC	400 $\pm$ 0.51	351.2 $\pm$ 0.65	368 $\pm$ 0.7	$⩽$0.01
Erdős Rényi	BC	400 $\pm$ 0.51	354.2 $\pm$ 0.65	365 $\pm$ 0.7	$⩽$0.01
Erdős Rényi	EC	400 $\pm$ 0.51	351.4 $\pm$ 0.21	367$\pm$ 0.92	$⩽$0.01
$G_{n,p}$ Random Graph	DC	400.18 $\pm$ 0.66	354.59 $\pm$ 0.61	365.44 $\pm$ 0,62	$⩽$0.01
$G_{n,p}$ Random Graph	BC	400.18 $\pm$ 0.66	357.79 $\pm$ 0.61	368.44 $\pm$ 0,62	$⩽$0.01
$G_{n,p}$ Random Graph	EC	400.18 $\pm$ 0.66	351.23 $\pm$ 0.21	371.44 $\pm$ 0.62	$⩽$0.01
Duplication Divergence	DC	15.2 $\pm$ 1.81	4.64 $\pm$ 0.52	15.08 $\pm$ 1.79	$⩽$0.01
Duplication Divergence	BC	15.2 $\pm$ 1.81	5.67 $\pm$ 0.51	14.88 $\pm$ 1.19	$⩽$0.01
Duplication Divergence	EC	15.2 $\pm$ 1.81	8.53 $\pm$ 1.4	17.14 $\pm$ 1.14	$⩽$0.01
Barabási-Albert	DC	129.38 $\pm$ 1.9	67.43 $\pm$ 3.45	115.85 $\pm$ 1.79	$⩽$0.01
Barabási-Albert	BC	129.38 $\pm$ 1.81	65.79 $\pm$ 0.51	120.98 $\pm$ 1.19	$⩽$0.01
Barabási-Albert	EC	129.38 $\pm$ 1.9	68.53 $\pm$ 1.4	117.14 $\pm$ 1.14	$⩽$0.01