Standard generating functions
The most common use for generating functions in network science is to
describe degree distributions. There are generating functions that
reflect to standard network generators provided by epydemic.
- epydemic.gf.gf_er(N, kmean=None, phi=None)
Return the generating function for the Poisson degree distribution of an ER network of N nodes with the given mean degree or occupation probability, as generated by
epydemic.ERNetwork.- Parameters:
N (
int) – the number of nodes in the networkkmean (
Optional[float]) – (optional) the mean degreephi (
Optional[float]) – (optional) the occupation probability
- Return type:
GF- Returns:
the generating function
- epydemic.gf.gf_powerlaw(exponent)
Return the generating function of the powerlaw degree distribution with the given exponent.
- Parameters:
exponent (
float) – the exponent of the distribution- Return type:
GF- Returns:
the generating function
- epydemic.gf.gf_ba(M)
Return the generating fuynction of the degree distribution of Barabasi-Albert network with connectivity of M edges per added node, as generated by class
epydemic.BANetwork. This is a powerlaw distribution with exponent 3.- Parameters:
M (
int) – the number of edges per node added- Return type:
GF- Returns:
the generating function
- epydemic.gf.gf_plc(exponent, cutoff)
Return the generating function of the powerlaw-with-cutoff degree distribution given its exponent and cutoff, as generated by
epydemic.PLCNetwork.- Parameters:
exponent (
float) – the exponent of the distributioncutoff (
float) – the cutoff
- Return type:
GF- Returns:
the generating function