# random.walk: Graph diffusion using a Markov random walk In diffusr: Network Diffusion Algorithms

### Description

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.

### Usage

 `1` ```random.walk(p0, graph, r = 0.5, ...) ```

### Arguments

 `p0` an `n`-dimensional numeric non-negative vector representing the starting distribution of the Markov chain (does not need to sum to one) `graph` an (`n x n`)-dimensional numeric non-negative adjacence matrix representing the graph `r` a scalar between (0, 1). restart probability if a Markov random walk with restart is desired `...` additional parameters

### Value

returns the stationary distribution as numeric vector

### Author(s)

Simon Dirmeier, simon.dirmeier@gmx.de

### References

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

### Examples

 ```1 2 3 4 5 6 7 8``` ```# count of nodes n <- 5 # starting distribution (has to sum to one) p0 <- as.vector(rmultinom(1, 1, prob=rep(.2, n))) # adjacency matrix (either normalized or not) graph <- matrix(abs(rnorm(n*n)), n, n) # computation of stationary distribution pt <- random.walk(p0, graph) ```

diffusr documentation built on May 19, 2017, 10:46 a.m.

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