Compute local scan statistics on graphs
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, ...)
- An igraph object, the graph for which the scan statistics will be computed
- An igraph object or
NULL, if not
NULL, then the
themstatistics is computed, i.e. the neighborhoods calculated from
graph.usare evaluated on
- An integer scalar, the size of the local neighborhood for each vertex. Should be non-negative.
- Character, a function name, or a function object itself, for
computing the local statistic in each neighborhood. If
NULL(the default value),
ecountis used for unweighted graphs (if
weighted=FALSE) and a function tha
- 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
- Character scalar, the kind of neighborhoods to use for the
calculation. One of
. This argument is ignored for undi
- A list of neighborhoods, one for each vertex, or
NULL. If it is not
NULL, then the function is evaluated on the induced subgraphs specified by these neighborhoods.
In theory this could be useful if the same
- Arguments passed to
FUN, the function that computes the local statistics.
See the given reference below for the details on the local scan statistics.
local_scan calculates exact local scan statistics.
local_scan computes the
graph.them should be an igraph object and the
graph.us to extract the neighborhood
information, and applying
FUN on these neighborhoods in
local_scantypically a numeric vector containing the computed local statistics for each vertex. In general a list or vector of objects, as returned by
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:
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)