# R igraph manual pages

Use this if you are using igraph from R

 knn {igraph} R Documentation

## Average nearest neighbor degree

### Description

Calculate the average nearest neighbor degree of the given vertices and the same quantity in the function of vertex degree

### Usage

``````knn(
graph,
vids = V(graph),
mode = c("all", "out", "in", "total"),
neighbor.degree.mode = c("all", "out", "in", "total"),
weights = NULL
)
``````

### Arguments

 `graph` The input graph. It may be directed. `vids` The vertices for which the calculation is performed. Normally it includes all vertices. Note, that if not all vertices are given here, then both ‘`knn`’ and ‘`knnk`’ will be calculated based on the given vertices only. `mode` Character constant to indicate the type of neighbors to consider in directed graphs. `out` considers out-neighbors, `in` considers in-neighbors and `all` ignores edge directions. `neighbor.degree.mode` The type of degree to average in directed graphs. `out` averages out-degrees, `in` averages in-degrees and `all` ignores edge directions for the degree calculation. `weights` Weight vector. If the graph has a `weight` edge attribute, then this is used by default. If this argument is given, then vertex strength (see `strength`) is used instead of vertex degree. But note that `knnk` is still given in the function of the normal vertex degree. Weights are are used to calculate a weighted degree (also called `strength`) instead of the degree.

### Details

Note that for zero degree vertices the answer in ‘`knn`’ is `NaN` (zero divided by zero), the same is true for ‘`knnk`’ if a given degree never appears in the network.

The weighted version computes a weighted average of the neighbor degrees as

`k_nn_u = 1/s_u sum_v w_uv k_v`,

where `s_u = sum_v w_uv` is the sum of the incident edge weights of vertex `u`, i.e. its strength. The sum runs over the neighbors `v` of vertex `u` as indicated by `mode`. `w_uv` denotes the weighted adjacency matrix and `k_v` is the neighbors' degree, specified by `neighbor_degree_mode`.

### Value

A list with two members:

 `knn` A numeric vector giving the average nearest neighbor degree for all vertices in `vids`. `knnk` A numeric vector, its length is the maximum (total) vertex degree in the graph. The first element is the average nearest neighbor degree of vertices with degree one, etc.

### Author(s)

Gabor Csardi csardi.gabor@gmail.com

### References

Alain Barrat, Marc Barthelemy, Romualdo Pastor-Satorras, Alessandro Vespignani: The architecture of complex weighted networks, Proc. Natl. Acad. Sci. USA 101, 3747 (2004)

### Examples

``````
# Some trivial ones
g <- make_ring(10)
knn(g)
g2 <- make_star(10)
knn(g2)

# A scale-free one, try to plot 'knnk'
g3 <- sample_pa(1000, m=5)
knn(g3)

# A random graph
g4 <- sample_gnp(1000, p=5/1000)
knn(g4)

# A weighted graph
g5 <- make_star(10)
E(g5)\$weight <- seq(ecount(g5))
knn(g5)
``````

[Package igraph version 1.3.2 Index]