$$Vi=\frac{E-Ei}{E}$$ E is the global efficiency and Ei is the global efficiency after the removal of the node i and its entire links.
c_net_stability(
go_ls,
mode = "robust_test",
partial = 0.5,
step = 10,
reps = 9,
threads = 1,
verbose = TRUE,
keystone = FALSE
)
robust_test(
go_ls,
partial = 0.5,
step = 10,
reps = 9,
threads = 1,
verbose = TRUE
)
vulnerability(go_ls, threads = 1, verbose = TRUE)
robustness(go_ls, keystone = FALSE, reps = 9, threads = 1, verbose = TRUE)Arguments
- go_ls
an igraph object or igraph list.
- mode
"robust_test", "vulnerability", "robustness"
- partial
how much percent vertexes be removed in total (default: 0.5, only for robust_test)
- step
how many nodes be removed each time? (default: 10, only for robust_test)
- reps
simulation number (default: 9)
- threads
threads
- verbose
verbose
- keystone
remove 70%% keystones instead of remove 50%% nodes (default: False, only for robustness)
Value
a data.frame
data.frame (robustness class)
a vector
Examples
# \donttest{
data("c_net")
if (requireNamespace("ggpmisc")) {
c_net_stability(co_net, mode = "robust_test", step = 20, reps = 9) -> robust_res
plot(robust_res, index = "Average_degree", mode = 2)
}
#> Loading required namespace: ggpmisc
c_net_stability(co_net, mode = "vulnerability") -> vulnerability_res
plot(vulnerability_res)
robustness(co_net) -> robustness_res
plot(robustness_res)
module_detect(co_net) -> co_net_modu
zp_analyse(co_net_modu, mode = 2) -> co_net_modu
c_net_stability(co_net_modu, mode = "robustness", keystone = TRUE) -> robustness_res
plot(robustness_res)
# }