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oddnet

The goal of oddnet is to identify anomalous networks from a series of temporal networks.

Installation

You can install the development version of oddnet from GitHub with:

# install.packages("devtools")
# devtools::install_github("sevvandi/oddnet")

Example

In this example we generate a series of networks and add an anomalous network at location 50.

library(oddnet)
library(igraph)
#> 
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:stats':
#> 
#>     decompose, spectrum
#> The following object is masked from 'package:base':
#> 
#>     union
set.seed(1)
networks <- list()
p.or.m.seq <- rep(0.05, 100)
p.or.m.seq[50] <- 0.2  # outlying network at 50
for(i in 1:100){
 gr <- igraph::erdos.renyi.game(100, p.or.m = p.or.m.seq[i])
 networks[[i]] <- igraph::as_adjacency_matrix(gr)
}
anom <- anomalous_networks(networks)
anom
#> Leave-out-out KDE outliers using lookout algorithm
#> 
#> Call: lookout::lookout(X = dfpca[, 1:dd], alpha = alpha)
#> 
#>   Outliers Probability
#> 1       50           0

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Version

Install

install.packages('oddnet')

Monthly Downloads

200

Version

0.1.1

License

GPL (>= 3)

Maintainer

Sevvandi Kandanaarachchi

Last Published

February 11th, 2024

Functions in oddnet (0.1.1)

oddnet-package

oddnet: Anomaly Detection in Temporal Networks
anomalous_networks

Identifies anomalous networks from a series of temporal networks.
lad

Laplacian Eigen Value method by Shenyang Huang, Yasmeen Hitti, Guillaume Rabusseau and Reihaneh Rabbany from their KDD'20 paper Laplacian Change Point Detection for Dynamic Graphs
compute_features

Computes features for each network.