igraph (version 1.3.5)

knn: 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
)

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.

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.

Author

Gabor Csardi

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.

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)