# Graph diffusion using a Markov random walk

### 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 |

`graph` |
an ( |

`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

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