These functions implement evolving network models based on different vertex types.
Arguments
- nodes
The number of vertices in the graph.
- types
The number of different vertex types.
- edge.per.step
The number of edges to add to the graph per time step.
- type.dist
The distribution of the vertex types. This is assumed to be stationary in time.
- pref.matrix
A matrix giving the preferences of the given vertex types. These should be probabilities, i.e. numbers between zero and one.
- directed
Logical constant, whether to generate directed graphs.
- ...
Passed to the constructor,
sample_traits()
orsample_traits_callaway()
.- k
The number of trials per time step, see details below.
Details
For sample_traits_callaway()
the simulation goes like this: in each
discrete time step a new vertex is added to the graph. The type of this
vertex is generated based on type.dist
. Then two vertices are
selected uniformly randomly from the graph. The probability that they will
be connected depends on the types of these vertices and is taken from
pref.matrix
. Then another two vertices are selected and this is
repeated edges.per.step
times in each time step.
For sample_traits()
the simulation goes like this: a single vertex is
added at each time step. This new vertex tries to connect to k
vertices in the graph. The probability that such a connection is realized
depends on the types of the vertices involved and is taken from
pref.matrix
.
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()
,
sample_pa_age()
,
sample_pref()
,
sample_sbm()
,
sample_smallworld()
,
sample_tree()
Author
Gabor Csardi csardi.gabor@gmail.com