This function calculates the IVI of the desired nodes from a graph. #' A shiny app has also been developed for the calculation of IVI as well as IVI-based network visualization, which is accessible using the `influential::runShinyApp("IVI")` command. You can also access the shiny app online at https://influential.erc.monash.edu/.

```
ivi(
graph,
vertices = V(graph),
weights = NULL,
directed = FALSE,
mode = "all",
loops = TRUE,
d = 3,
scaled = TRUE
)
```

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

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

- weights
Optional positive weight vector for calculating weighted betweenness centrality of nodes as a requirement for calculation of IVI. If the graph has a weight edge attribute, then this is used by default. Weights are used to calculate weighted shortest paths, so they are interpreted as distances.

- directed
Logical scalar, whether to directed graph is analyzed. This argument is ignored for undirected graphs.

- mode
The mode of IVI depending on the directedness of the graph. If the graph is undirected, the mode "all" should be specified. Otherwise, for the calculation of IVI based on incoming connections select "in" and for the outgoing connections select "out". Also, if all of the connections are desired, specify the "all" mode. Default mode is set to "all".

- loops
Logical; whether the loop edges are also counted.

- d
The distance, expressed in number of steps from a given node (default=3). Distance must be > 0. According to Morone & Makse (https://doi.org/10.1038/nature14604), optimal results can be reached at d=3,4, but this depends on the size/"radius" of the network. NOTE: the distance d is not inclusive. This means that nodes at a distance of 3 from our node-of-interest do not include nodes at distances 1 and 2. Only 3.

- scaled
Logical; whether the end result should be 1-100 range normalized or not (default is TRUE).

A numeric vector with the IVI values based on the provided centrality measures.

Other integrative ranking functions:
`comp_manipulate()`

,
`exir()`

,
`hubness.score()`

,
`ivi.from.indices()`

,
`spreading.score()`

```
if (FALSE) {
MyData <- coexpression.data
My_graph <- graph_from_data_frame(MyData)
GraphVertices <- V(My_graph)
My.vertices.IVI <- ivi(graph = My_graph, vertices = GraphVertices,
weights = NULL, directed = FALSE, mode = "all",
loops = TRUE, d = 3, scaled = TRUE)
}
```