Use this if you are using igraph from R
This is a growing network model, which resembles of how the forest fire spreads by igniting trees close by.
sample_forestfire(nodes, fw.prob, bw.factor = 1, ambs = 1, directed = TRUE)
The number of vertices in the graph.
The forward burning probability, see details below.
The backward burning ratio. The backward burning
probability is calculated as
The number of ambassador vertices.
Logical scalar, whether to create a directed graph.
The forest fire model intends to reproduce the following network characteristics, observed in real networks:
Heavy-tailed in-degree distribution.
Heavy-tailed out-degree distribution.
Densification power-law. The network is densifying in time, according to a power-law rule.
Shrinking diameter. The diameter of the network decreases in time.
The network is generated in the following way. One vertex is added at a
time. This vertex connects to (cites)
ambs vertices already present
in the network, chosen uniformly random. Now, for each cited vertex v
we do the following procedure:
We generate two random
number, x and y, that are geometrically distributed with means
p/(1-p) and rp(1-rp). (p is
fw.prob, r is
bw.factor.) The new vertex cites x outgoing neighbors and
y incoming neighbors of v, from those which are not yet cited by
the new vertex. If there are less than x or y such vertices
available then we cite all of them.
The same procedure is applied to all the newly cited vertices.
A simple graph, possibly directed if the
directed argument is
The version of the model in the published paper is incorrect in the sense that it cannot generate the kind of graphs the authors claim. A corrected version is available from http://www.cs.cmu.edu/~jure/pubs/powergrowth-tkdd.pdf, our implementation is based on this.
Gabor Csardi email@example.com
Jure Leskovec, Jon Kleinberg and Christos Faloutsos. Graphs over time: densification laws, shrinking diameters and possible explanations. KDD '05: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 177–187, 2005.
barabasi.game for the basic preferential attachment
g <- sample_forestfire(10000, fw.prob=0.37, bw.factor=0.32/0.37) dd1 <- degree_distribution(g, mode="in") dd2 <- degree_distribution(g, mode="out") plot(seq(along.with=dd1)-1, dd1, log="xy") points(seq(along.with=dd2)-1, dd2, col=2, pch=2)