Description Usage Arguments Details Value Author(s) References Examples
Computes the evolution of the size of the largest connex component and its mean minimum path length under a random or targeted attack of a graph.
1 2 3 | random.attack(adj.mat, max.remove, nsim)
node.attack(adj.mat, node.sup)
targeted.attack(adj.mat, max.remove)
|
adj.mat |
adjacency matrix of the graph. |
max.remove |
maximum number of nodes to be removed |
nsim |
number of simulation of random attack to be processed |
node.sup |
number of the node to be removed |
An attack consists in removing a node and all its connections from a given graph. random.attack
removes max.remove
nodes by selecting one regional node at random and removing it (and all its connections) from the graph. targeted.attack
removes max.remove
nodes : the first node to be eliminated was the hub with the largest degree, and nodes were subsequently eliminated in rank order of decreasing degree.
size.large.connex |
vector containing the evolution of the size of the largest connexe component during an attack. |
charac.path.length |
vector containing the evolution of the mean minimum path length of the largest connexe component during an attack. |
S. Achard
Albert R., Barabasi A. L. (2002) Statistical mechanics of complex networks. Rev Mod Physics 74 pages 47-97.
S. Achard, R. Salvador, B. Whitcher, J. Suckling, Ed Bullmore (2006) A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs. Journal of Neuroscience, Vol. 26, N. 1, pages 63-72.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | set2<-array(c(5,6.5,7,6.5,5,3.5,3,3.5,1,1.5,3,4.5,5,4.5,3,1.5),dim=c(8,2))
names<-c(1:8)
mat<-sim.rand(8,20)
#simulated attack on node 1
plot(set2[,1], set2[,2], type = "n",xlab="", ylab="",cex.lab=1.5)
text(set2[2:8,1], set2[2:8,2], names[2:8], cex = 1.5)
text(set2[1,1], set2[1,2], 1 , cex = 1.5,col="gray")
for(k in 2:8){
for(q in 1:(k-1)){
if(mat[k,q]==1)
{
if(q==1) visu <- "gray" else visu <- "red"
lines(c(set2[k,1], set2[q,1]), c(set2[k,2], set2[q,2]), col = visu)
}
}
}
ra<-random.attack(mat,8,10)
na<-node.attack(mat, 1)
ta<-targeted.attack(mat,8)
|
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