# knn

0th

Percentile

##### 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
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 knn and knnk will be calculated based on the given
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
##### 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.

##### Value

• A list with two members:
• knnA numeric vector giving the average nearest neighbor degree for all vertices in vids.
• knnkA 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)

• graph.knn
• knn
##### 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)
Documentation reproduced from package igraph, version 1.0.0, License: GPL (>= 2)

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