Description Usage Arguments Value References Examples
A Markov Random Walk takes an inital distribution p0
and calculates the stationary distribution of that.
The diffusion process is regulated by a restart probability r
which
controls how often the MRW jumps back to the initial values.
1 2 3 4 5 6 7 8 9 10  random.walk(p0, graph, r = 0.5, niter = 10000, thresh = 1e04,
do.analytical = FALSE, correct.for.hubs = FALSE)
## S4 method for signature 'numeric,matrix'
random.walk(p0, graph, r = 0.5, niter = 10000,
thresh = 1e04, do.analytical = FALSE, correct.for.hubs = FALSE)
## S4 method for signature 'matrix,matrix'
random.walk(p0, graph, r = 0.5, niter = 10000,
thresh = 1e04, do.analytical = FALSE, correct.for.hubs = FALSE)

p0 
an 
graph 
an ( 
r 
a scalar between (0, 1). restart probability if a Markov random walk with restart is desired 
niter 
maximal number of iterations for computation of the
Markov chain. If 
thresh 
threshold for breaking the iterative computation of the
stationary distribution. If the absolute difference of the distribution at
time point $t1$ and $t$ is less than 
do.analytical 
boolean if the stationary distribution shall be computed solving the analytical solution or rather iteratively 
correct.for.hubs 
if P(j  i) = 1 /degree(i) * min(1, degree(j)/degree(j)) Note that this will not consider edge weights. 
returns a list with the following elements
p.inf the stationary distribution as numeric vector
transition.matrix the column normalized transition matrix used for the random walk
Tong, H., Faloutsos, C., & Pan, J. Y. (2006),
Fast random walk with restart and its applications.
Koehler, S., Bauer, S., Horn, D., & Robinson, P. N. (2008),
Walking the interactome for prioritization of candidate disease genes.
The American Journal of Human Genetics
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