Generate an evolving random graph with preferential attachment and aging
This function creates a random graph by simulation its evolution. Each time a new vertex is added it creates a number of links to old vertices and the probability that an old vertex is cited depends on its in-degree (preferential attachment) and age.
aging.prefatt.game(n, m=1, aging.type="exponential", params=list(), ...)
- The number of vertices in the graph.
- The number of edges each new vertex creates (except the very first vertex.
- Character string, the type of the function giving probability that an old vertex is cited depending on its age. See details below.
- Named list, this gives the parameters of the
aging functionselected by the
aging.typeargument. See details below.
- Additional arguments, these are passed to the graph constructor.
This is discrete time step model, in each time step a new vertex is
added to the network. The new vertex cites a number (parameter
m) of other vertices. The probability that a vertex is cited is
proportional to the product of the in-degree of the node plus one and
the so-called aging function (
exponential aging function decreases exponetially with age
aging.exp, its only parameter.
powerlaw aging function decreases as a power law with age,
the exponent is given by the
- A new graph.
g <- aging.prefatt.game(100, aging.type="exponential", params=list(aging.exp=1/100)) g2 <- aging.prefatt.game(100, aging.type="powerlaw", params=list(aging.exp=1))