R igraph manual pages

Random walk on a graph

Description

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.

Usage

random_walk(
  graph,
  start,
  steps,
  mode = c("out", "in", "all", "total"),
  stuck = c("return", "error")
)

random_edge_walk(
  graph,
  start,
  steps,
  weights = NULL,
  mode = c("out", "in", "all", "total"),
  stuck = c("return", "error")
)

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.

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.

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.

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)

## But these are (almost) the same
cor(table(w), pg)

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