probinet.synthetic.anomaly

probinet.synthetic.anomaly#

Class for generation and management of synthetic networks with anomalies

Classes

SyntNetAnomaly([m, N, K, avg_degree, ...])

Class for generation and management of synthetic networks with anomalies.
class probinet.synthetic.anomaly.SyntNetAnomaly(m: int = 1, N: int = 100, K: int = 2, avg_degree: float = 4.0, rho_anomaly: float = 0.1, structure: str = 'assortative', label: str | None = None, pi: float = 0.8, eta: float = 0.5, L1: bool = False, ag: float = 0.6, bg: float = 1.0, corr: float = 0.0, over: float = 0.0, verbose: int = 0, out_folder: Path = PosixPath('outputs'), output_parameters: bool = False, output_adj: bool = False, outfile_adj: str | None = None, rng: Generator | None = None)[source]#

Class for generation and management of synthetic networks with anomalies.

anomaly_network_PB(parameters=None)[source]#

Generate a directed, possibly weighted network by using the anomaly models Poisson-Poisson.

Steps:

  1. Generate or load the latent variables Z_ij.

  2. Extract A_ij entries (network edges) from a Poisson (M_ij) distribution if Z_ij=0; from a Poisson (pi) distribution if Z_ij=1.

Parameters:

parameters – Latent variables z, s, u, v, and w.

Returns:

G – DiGraph NetworkX object. Self-loops allowed.

Return type:

DiGraph