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This function identifies anomalous networks from a series of temporal networks. It uses graph theoretic features to transform networks to a feature space. This function has parameters for feature computation, scaling, robust PCA and anomaly detection procedures. ADD MORE DESCRIPTION.

Usage

anomalous_networks(
  networks,
  alpha = 0.05,
  dd = 2,
  trim = 0.005,
  na_action = NULL,
  vert_attr = FALSE,
  attr_name = NULL,
  attr_mat = NULL,
  fast = FALSE
)

Arguments

networks

The input series of temporal networks given in a list with each network denoted by its adjacency matrix.

alpha

An anomaly detection parameter. The level of significance for the anomaly detection algorithm lookout. Default is 0.05.

dd

A robust PCA parameter. The number of reduced dimensions in robust PCA. Default is 2.

trim

A scaling parameter. The percentage used to compute trimmed mean and trimmed standard deviation. Default is 0.5 percent.

na_action

The action for NA valued features.

vert_attr

A feature computation parameter. If TRUE the network nodes/vertices have attributes.

attr_name

A feature computation parameter. The name of the network vertex attribute. Only a single attribute can be specified.

attr_mat

A feature computation parameter. If network nodes/vertices have attributes, the list of attribute matrices for each network can be given using this feature.

fast

If set to TRUE will avoid computing time consuming features.

Value

Object imported from lookout.

See also

[lookout::lookout()]

Examples

# We generate a series of networks and add an anomaly at 50th network.
set.seed(1)
networks <- list()
p.or.m.seq <- rep(0.1, 50)
p.or.m.seq[20] <- 0.3  # anomalous network at 20
for(i in 1:50){
  gr <- igraph::erdos.renyi.game(50, p.or.m = p.or.m.seq[i])
  networks[[i]] <- igraph::as_adjacency_matrix(gr)
}
anomalous_networks(networks, fast = TRUE)
#> Leave-out-out KDE outliers using lookout algorithm
#> 
#> Call: lookout::lookout(X = dfpca[, 1:dd], alpha = alpha)
#> 
#>   Outliers Probability
#> 1       20           0
#>