NOESIS: A Framework for Complex Network Data Analysis

Table 3

Computational time complexity and bibliographic references for the link scoring and prediction methods provided by NOESIS.

Type	Name	Complexity	Reference
Local	Common neighbors count		[59]
	Adamic–Adar score		[60]
	Resource-allocation index		[61]
	Adaptive degree penalization score		[62]
	Jaccard score		[63]
	Leicht–Holme–Newman score		[64]
	Salton score		[65]
	Sorensen score		[66]
	Hub promoted index		[67]
	Hub depressed index		[67]
	Preferential attachment score		[68]
Global	Katz index		[34]
	Leicht–Holme–Newman score		[64]
	Random walk		[69]
	Random walk with restart		[70]
	Flow propagation		[71]
	Pseudoinverse Laplacian score		[72]
	Average commute time score		[72]
	Random forest kernel index		[73]

In the time complexity analysis, is the number of nodes in the network, d is the maximum node degree, and c refers to the number of iterations required by iterative global link prediction methods.