irefit: Iterative refitting

In simplifyNet: Network Sparsification

Iterative refitting

Description

Iterative sparsifcation based refitting.

Usage

irefit(
  network,
  func,
  tol,
  rank = "none",
  connected = FALSE,
  directed = FALSE,
  per = 0.5
)

Arguments

network	Weighted adjacency matrix, weighted igraph network, or edge list formatted | n1 | n2 | weight | with colnames c("n1", "n2", "weight").
func	Model function whose input is the network and whose output is a single real value or a list of reevaluated weights in the first index and a real value in the second. A wrapper function may have to be written.
tol	Allowed error around the original output of func approximated by the sparsified network within which edges are removed. Specifies if method converges.
rank	Ranking of edges. Lower ranked edges are removed first. Must be the same length as nrow(E_List).
connected	If TRUE, connectivity of the network is prioritized over scoring by func.
directed	If TRUE, specifies that the inputted network is directed. Default is FALSE.
per	Percentage of edges to add/remove from the sparsifier at each step.

Value

Sparsified network, H, which still maintains evaluator function, func, plus/minus tol.

Author(s)

Alexander Mercier

Andrew Kramer

Examples

#Set scoring function
mean.weight.degree <- function(graph){
graph.ob <- igraph::graph_from_edgelist(graph[,1:2])
igraph::E(graph.ob)$weight <- graph[,3]
return(mean(igraph::strength(graph.ob)))
}

#Generate random graph
g <- igraph::erdos.renyi.game(100, 0.1)
igraph::E(g)$weight <- rexp(length(igraph::E(g)), rate=10) #random edge weights from exp(10)
E_List <- cbind(igraph::as_edgelist(g), igraph::E(g)$weight)
colnames(E_List) <- c("n1", "n2", "weight")
sparse_dist <- simplifyNet::irefit(E_List, func=mean.weight.degree, tol = 0.1)

simplifyNet documentation built on Nov. 15, 2022, 5:05 p.m.