`random_walk()`

performs a random walk on the graph and returns the
vertices that the random walk passed through. `random_edge_walk()`

is the same but returns the edges that that random walk passed through.

## Arguments

- graph
The input graph, might be undirected or directed.

- start
The start vertex.

- steps
The number of steps to make.

- mode
How to follow directed edges.

`"out"`

steps along the edge direction,`"in"`

is opposite to that.`"all"`

ignores edge directions. This argument is ignored for undirected graphs.- stuck
What to do if the random walk gets stuck.

`"return"`

returns the partial walk,`"error"`

raises an error.- weights
The edge weights. Larger edge weights increase the probability that an edge is selected by the random walker. In other words, larger edge weights correspond to stronger connections. The ‘weight’ edge attribute is used if present. Supply ‘

`NA`

’ here if you want to ignore the ‘weight’ edge attribute.

## Value

For `random_walk()`

, a vertex sequence containing the vertices
along the walk. For `random_edge_walk()`

, an edge sequence containing
the edges along the walk.

## Details

Do a random walk. From the given start vertex, take the given number of
steps, choosing an edge from the actual vertex uniformly randomly. Edge
directions are observed in directed graphs (see the `mode`

argument
as well). Multiple and loop edges are also observed.

## Examples

```
## Stationary distribution of a Markov chain
g <- make_ring(10, directed = TRUE) %u%
make_star(11, center = 11) + edge(11, 1)
ec <- eigen_centrality(g, directed = TRUE)$vector
pg <- page_rank(g, damping = 0.999)$vector
w <- random_walk(g, start = 1, steps = 10000)
## These are similar, but not exactly the same
cor(table(w), ec)
#> [1] 0.9623762
## But these are (almost) the same
cor(table(w), pg)
#> [1] 0.9999923
```