Preferential attachment is a family of simple stochastic algorithms for building a graph. Variants include the Barabási-Abert model and the Price model.
Usage
sample_pa(
n,
power = 1,
m = NULL,
out.dist = NULL,
out.seq = NULL,
out.pref = FALSE,
zero.appeal = 1,
directed = TRUE,
algorithm = c("psumtree", "psumtree-multiple", "bag"),
start.graph = NULL
)
pa(...)
Arguments
- n
Number of vertices.
- power
The power of the preferential attachment, the default is one, i.e. linear preferential attachment.
- m
Numeric constant, the number of edges to add in each time step This argument is only used if both
out.dist
andout.seq
are omitted or NULL.- out.dist
Numeric vector, the distribution of the number of edges to add in each time step. This argument is only used if the
out.seq
argument is omitted or NULL.- out.seq
Numeric vector giving the number of edges to add in each time step. Its first element is ignored as no edges are added in the first time step.
- out.pref
Logical, if true the total degree is used for calculating the citation probability, otherwise the in-degree is used.
- zero.appeal
The ‘attractiveness’ of the vertices with no adjacent edges. See details below.
- directed
Whether to create a directed graph.
- algorithm
The algorithm to use for the graph generation.
psumtree
uses a partial prefix-sum tree to generate the graph, this algorithm can handle anypower
andzero.appeal
values and never generates multiple edges.psumtree-multiple
also uses a partial prefix-sum tree, but the generation of multiple edges is allowed. Before the 0.6 version igraph used this algorithm ifpower
was not one, orzero.appeal
was not one.bag
is the algorithm that was previously (before version 0.6) used ifpower
was one andzero.appeal
was one as well. It works by putting the ids of the vertices into a bag (multiset, really), exactly as many times as their (in-)degree, plus once more. Then the required number of cited vertices are drawn from the bag, with replacement. This method might generate multiple edges. It only works ifpower
andzero.appeal
are equal one.- start.graph
NULL
or an igraph graph. If a graph, then the supplied graph is used as a starting graph for the preferential attachment algorithm. The graph should have at least one vertex. If a graph is supplied here and theout.seq
argument is notNULL
, then it should contain the out degrees of the new vertices only, not the ones in thestart.graph
.- ...
Passed to
sample_pa()
.
Details
This is a simple stochastic algorithm to generate a graph. It is a discrete time step model and in each time step a single vertex is added.
We start with a single vertex and no edges in the first time step. Then we
add one vertex in each time step and the new vertex initiates some edges to
old vertices. The probability that an old vertex is chosen is given by
P[i]∼kαi+a where ki
is the in-degree of vertex i in the current time step (more precisely
the number of adjacent edges of i which were not initiated by i
itself) and α and a are parameters given by the
power
and zero.appeal
arguments.
The number of edges initiated in a time step is given by the m
,
out.dist
and out.seq
arguments. If out.seq
is given and
not NULL then it gives the number of edges to add in a vector, the first
element is ignored, the second is the number of edges to add in the second
time step and so on. If out.seq
is not given or null and
out.dist
is given and not NULL then it is used as a discrete
distribution to generate the number of edges in each time step. Its first
element is the probability that no edges will be added, the second is the
probability that one edge is added, etc. (out.dist
does not need to
sum up to one, it normalized automatically.) out.dist
should contain
non-negative numbers and at east one element should be positive.
If both out.seq
and out.dist
are omitted or NULL then m
will be used, it should be a positive integer constant and m
edges
will be added in each time step.
sample_pa()
generates a directed graph by default, set
directed
to FALSE
to generate an undirected graph. Note that
even if an undirected graph is generated ki denotes the number
of adjacent edges not initiated by the vertex itself and not the total
(in- + out-) degree of the vertex, unless the out.pref
argument is set to
TRUE
.
References
Barabasi, A.-L. and Albert R. 1999. Emergence of scaling in random networks Science, 286 509--512.
de Solla Price, D. J. 1965. Networks of Scientific Papers Science, 149 510--515.
See also
Random graph models (games)
erdos.renyi.game()
,
sample_()
,
sample_bipartite()
,
sample_correlated_gnp()
,
sample_correlated_gnp_pair()
,
sample_degseq()
,
sample_dot_product()
,
sample_fitness()
,
sample_fitness_pl()
,
sample_forestfire()
,
sample_gnm()
,
sample_gnp()
,
sample_grg()
,
sample_growing()
,
sample_hierarchical_sbm()
,
sample_islands()
,
sample_k_regular()
,
sample_last_cit()
,
sample_pa_age()
,
sample_pref()
,
sample_sbm()
,
sample_smallworld()
,
sample_traits_callaway()
,
sample_tree()
Random graph models (games)
erdos.renyi.game()
,
sample_()
,
sample_bipartite()
,
sample_correlated_gnp()
,
sample_correlated_gnp_pair()
,
sample_degseq()
,
sample_dot_product()
,
sample_fitness()
,
sample_fitness_pl()
,
sample_forestfire()
,
sample_gnm()
,
sample_gnp()
,
sample_grg()
,
sample_growing()
,
sample_hierarchical_sbm()
,
sample_islands()
,
sample_k_regular()
,
sample_last_cit()
,
sample_pa_age()
,
sample_pref()
,
sample_sbm()
,
sample_smallworld()
,
sample_traits_callaway()
,
sample_tree()
Author
Gabor Csardi csardi.gabor@gmail.com
Examples
g <- sample_pa(10000)
degree_distribution(g)
#> [1] 0.0000 0.6718 0.1603 0.0664 0.0327 0.0208 0.0128 0.0064 0.0065 0.0035
#> [11] 0.0028 0.0020 0.0021 0.0023 0.0008 0.0014 0.0014 0.0009 0.0003 0.0002
#> [21] 0.0002 0.0004 0.0004 0.0000 0.0001 0.0003 0.0001 0.0002 0.0003 0.0004
#> [31] 0.0001 0.0000 0.0001 0.0003 0.0001 0.0001 0.0000 0.0000 0.0000 0.0000
#> [41] 0.0000 0.0002 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> [51] 0.0001 0.0000 0.0000 0.0001 0.0000 0.0001 0.0000 0.0000 0.0000 0.0001
#> [61] 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> [71] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0003 0.0000 0.0000
#> [81] 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> [91] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000
#> [101] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> [111] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> [121] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> [131] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> [141] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001