stat.deletions {ANTs}R Documentation

Network target & random deletion simulations


Perfoms a knockout analysis according to specific vertex attributes and a specific percentage of nodes to delete


  weighted = TRUE,
  shortest.weight = FALSE,
  normalization = FALSE,
  directed = TRUE,
  out = TRUE,
  progress = TRUE,
  return.mat = FALSE



a square adjacency M.


a vector of categorical (factor or character) or numeric (continuous) attributes of the nodes. The vector must have the same length and order as the nodes in the square adjacency matrix 'M'.


Indicates the nodes that will be the target of deletion. If the argument 'attr' is categorical, then 'target' indicates the attribute of the node target of deletion. If the argument 'attr' is numeric, then 'target' can take one of two character elements 1) 'decreasing' or 2) 'increasing' indicating whether the target of deletions are nodes with the greatest or lowest attribute's values respectively.


an integer indicating the number of deletions to perform in each simulation.


an integer indicating the number of simulations, i.e. how many times to perform ndel deletions.


if true, it binarizes the square adjacency matrix M. Geodesic distances and diameter are based only on the presence or absence of edges.


if false, it considers the higher met.strength as the shortest path.


normalizes the weights of the links i.e. divides them by the average strength of the network. Argument normalization can't be TRUE when argument weighted is FALSE.


if false, then it symmetrizes the matrix. Otherwise, it calculates geodesic distances and diameter according to the directionality of the links.


if true, it considers outgoing ties for the diameter and the geodesic distances.


a boolean if TRUE it prints the progress of the simulations.


a boolean if TRUE it returns a list of two elements : 1) a list of matrix deletions through target deletion; 2) a list of matrix deletions through random deletion


Knockout analysis allows the study of resilience properties of networks when specific nodes are removed. It is usually compared with random deletions.


A list of two elements:


Sebastian Sosa, Ivan Puga-Gonzalez.


Lusseau D. 2003. The emergent properties of a dolphin social network. Proceedings of the Royal Society of London Series B: Biological Sciences 270(Suppl 2):S186-S188.

Manno TG. 2008. Social networking in the Columbian ground squirrel, Spermophilus columbianus. Animal Behaviour 75(4):1221-1228.

Kanngiesser P, Sueur C, Riedl K, Grossmann J, Call J. 2011. Grooming network cohesion and the role of individuals in a captive chimpanzee group. American journal of primatology 73(8):758-767.

Sosa S. 2014. Structural Architecture of the Social Network of a Non-Human Primate (Macaca sylvanus): A Study of Its Topology in La Foret des Singes, Rocamadour. Folia Primatologica 85(3):154-163.

Sosa, S. (2018). Social Network Analysis, in: Encyclopedia of Animal Cognition and Behavior. Springer.


# Simulating data
m <- matrix(sample(c(0:5), 50 * 50, TRUE), 50, 50)
diag(m) <- 0
mb <- mat.binaryzation(m)

# Weighted categorical attribute example
attr <- sample(c("a", "b"), 50, TRUE)
t <- stat.deletions(m, attr = attr, target = "a", nsim = 2, ndel = 10)
t <- stat.deletions(mb, attr = attr, target = "a", nsim = 2, ndel = 10)
#  continous attribute example
attr <- c(sample(c(1:10), 50, TRUE))

t <- stat.deletions(m, attr = attr, target = "decreasing", nsim = 2, ndel = 4)
t <- stat.deletions(mb, attr = attr, target = "decreasing", nsim = 2, ndel = 4)

[Package ANTs version 0.0.16 Index]