Use this if you are using igraph from R

The scan statistic is a summary of the locality statistics that is
computed from the local neighborhood of each vertex. The
`local_scan`

function computes the local statistics for each vertex
for a given neighborhood size and the statistic function.

local_scan( graph.us, graph.them = NULL, k = 1, FUN = NULL, weighted = FALSE, mode = c("out", "in", "all"), neighborhoods = NULL, ... )

`graph.us, graph` |
An igraph object, the graph for which the scan statistics will be computed |

`graph.them` |
An igraph object or |

`k` |
An integer scalar, the size of the local neighborhood for each vertex. Should be non-negative. |

`FUN` |
Character, a function name, or a function object itself, for
computing the local statistic in each neighborhood. If |

`weighted` |
Logical scalar, TRUE if the edge weights should be used
for computation of the scan statistic. If TRUE, the graph should be
weighted. Note that this argument is ignored if |

`mode` |
Character scalar, the kind of neighborhoods to use for the
calculation. One of ‘ |

`neighborhoods` |
A list of neighborhoods, one for each vertex, or
In theory this could be useful if the same |

`...` |
Arguments passed to |

See the given reference below for the details on the local scan statistics.

`local_scan`

calculates exact local scan statistics.

If `graph.them`

is `NULL`

, then `local_scan`

computes the
‘us’ variant of the scan statistics. Otherwise,
`graph.them`

should be an igraph object and the ‘them’
variant is computed using `graph.us`

to extract the neighborhood
information, and applying `FUN`

on these neighborhoods in
`graph.them`

.

For `local_scan`

typically a numeric vector containing the
computed local statistics for each vertex. In general a list or vector
of objects, as returned by `FUN`

.

Priebe, C. E., Conroy, J. M., Marchette, D. J., Park,
Y. (2005). Scan Statistics on Enron Graphs. *Computational and
Mathematical Organization Theory*.

Other scan statistics:
`scan_stat()`

pair <- sample_correlated_gnp_pair(n = 10^3, corr = 0.8, p = 0.1) local_0_us <- local_scan(graph.us = pair$graph1, k = 0) local_1_us <- local_scan(graph.us = pair$graph1, k = 1) local_0_them <- local_scan(graph.us = pair$graph1, graph.them = pair$graph2, k = 0) local_1_them <- local_scan(graph.us = pair$graph1, graph.them = pair$graph2, k = 1) Neigh_1 <- neighborhood(pair$graph1, order = 1) local_1_them_nhood <- local_scan(graph.us = pair$graph1, graph.them = pair$graph2, neighborhoods = Neigh_1)

[Package *igraph* version 1.2.5 Index]