# graph.knn

From igraph v0.6.5-2
by Gabor Csardi

##### Average nearest neighbor degree

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

- Keywords
- graphs

##### Usage

`graph.knn(graph, vids=V(graph), weights=NULL)`

##### Arguments

- graph
- The input graph. It can be directed, but it will be treated as undirected, i.e. the direction of the edges is ignored.
- 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
and`knn`

will be calculated based`knnk`

- 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`graph.strength`

) is used instead of

##### Details

Note that for zero degree vertices the answer in `knn`

`NaN`

(zero divided by zero), the same is true for
`knnk`

##### 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.

##### 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 <- graph.ring(10)
graph.knn(g)
g2 <- graph.star(10)
graph.knn(g2)
# A scale-free one, try to plot 'knnk'
g3 <- ba.game(1000, m=5)
graph.knn(g3)
# A random graph
g4 <- random.graph.game(1000, p=5/1000)
graph.knn(g4)
# A weighted graph
g5 <- graph.star(10)
E(g5)$weight <- seq(ecount(g5))
graph.knn(g5)
```

*Documentation reproduced from package igraph, version 0.6.5-2, License: GPL (>= 2)*

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