This function is achieved by the integration susceptible-infected-recovered (SIR) model with the leave-one-out cross validation technique and ranks network nodes based on their true universal influence. One of the applications of this function is the assessment of performance of a novel algorithm in identification of network influential nodes by considering the SIRIR ranks as the ground truth (gold standard).

- graph
A graph (network) of the igraph class.

- vertices
A vector of desired vertices, which could be obtained by the V function.

- beta
Non-negative scalar. The rate of infection of an individual that is susceptible and has a single infected neighbor. The infection rate of a susceptible individual with n infected neighbors is n times beta. Formally this is the rate parameter of an exponential distribution.

- gamma
Positive scalar. The rate of recovery of an infected individual. Formally, this is the rate parameter of an exponential distribution.

- no.sim
Integer scalar, the number of simulation runs to perform SIR model on for the original network as well perturbed networks generated by leave-one-out technique. You may choose a different no.sim based on the available memory on your system.

- seed
A single value, interpreted as an integer to be used for random number generation.

A two-column dataframe; a column containing the difference values of the original and perturbed networks and a column containing node influence rankings

`cent_network.vis`

,
and `sir`

for a complete description on SIR model

Other centrality functions:
`betweenness()`

,
`clusterRank()`

,
`collective.influence()`

,
`degree()`

,
`h_index()`

,
`lh_index()`

,
`neighborhood.connectivity()`

```
set.seed(1234)
My_graph <- igraph::sample_gnp(n=50, p=0.05)
GraphVertices <- V(My_graph)
Influence.Ranks <- sirir(graph = My_graph, vertices = GraphVertices,
beta = 0.5, gamma = 1, no.sim = 10, seed = 1234)
```