glp: Run Generic Label Propagation (GLP) with a fixed parameter
In miguelbiron/threepRop: R Implementation of the 3prop Label Propagation Algorithm

Description Usage Arguments Value References Examples

This function runs the iterative procedure necessary to compute GLP for a given parameter lambda, exactly as in Equation 1 in Mostafavi et al. (2012).

1	glp(M, y, lambda, tol = 1e-04, max_iter = 100L, verbose = 0L)

`M`	normalized affinity matrix, as returned by `normalize_A()`, of size `N x N`. Must be of class `sparseMatrix`.
`y`	vector of labels, of length `N`. Must be of class `sparseVector`.
`lambda`	GLP parameter (between 0 and 1).
`tol`	tolerance for the stopping creiterion.
`max_iter`	maximum number of iterations.
`verbose`	print every `verbose` (0 means no output).

A vector of length N with the estimated scores.

Mostafavi, S., Goldenberg, A., & Morris, Q. (2012). Labeling nodes using three degrees of propagation. PloS one, 7(12), e51947.

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sim_SBM = simulate_simple_SBM(N = 2500L, p_1 = 0.2, D = 0.04, R = 0.25)
M = normalize_A(sim_SBM$A, "asym")
glp(M=M, y=sim_SBM$y, lambda=0.1, max_iter = 10L, verbose = 1L)