probinet.models.jointcrep

probinet.models.jointcrep#

Class definition of JointCRep, the algorithm to perform inference in networks with reciprocity. The latent variables are related to community memberships and a pair interaction value [CSDB22].

Classes

JointCRep(**kwargs)

Class definition of JointCRep, the algorithm to perform inference in networks with reciprocity.
class probinet.models.jointcrep.JointCRep(**kwargs: Any)[source]#

Class definition of JointCRep, the algorithm to perform inference in networks with reciprocity.

compute_likelihood() → float[source]#

Compute the log-likelihood of the data.

Returns:

loglik – Log-likelihood value.

Return type:

float

eta_fix_point() → float[source]#

Compute the fix point of the pair interaction coefficient eta. :returns: eta – Fix point of the pair interaction coefficient eta. :rtype: float

fit(gdata: GraphData, K: int = 3, initialization: int = 0, eta0: float | None = None, undirected: bool = False, assortative: bool = True, fix_eta: bool = False, fix_communities: bool = False, fix_w: bool = False, use_approximation: bool = False, out_inference: bool = True, out_folder: Path = PosixPath('outputs'), end_file: str | None = None, files: PathLike | None = None, rng: Generator | None = None, **_kwargs: Any) → tuple[ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]], float, float][source]#

Model directed networks by using a probabilistic generative model based on a Bivariate Bernoulli distribution that assumes community parameters and a pair interaction coefficient as latent variables. The inference is performed via the EM algorithm.

Parameters:

  • gdata – Graph adjacency tensor.

  • K – Number of communities, by default 3.

  • initialization – Indicator for choosing how to initialize u, v, and w. If 0, they will be generated randomly; 1 means only the affinity matrix w will be uploaded from file; 2 implies the membership matrices u and v will be uploaded from file, and 3 all u, v, and w will be initialized through an input file, by default 0.

  • eta0 – Initial value for the reciprocity coefficient, by default None.

  • undirected – Flag to call the undirected network, by default False.

  • assortative – Flag to call the assortative network, by default True.

  • fix_eta – Flag to fix the eta parameter, by default False.

  • fix_communities – Flag to fix the community memberships, by default False.

  • fix_w – Flag to fix the affinity tensor, by default False.

  • use_approximation – Flag to use approximation in updates, by default False.

  • out_inference – Flag to evaluate inference results, by default True.

  • out_folder – Output folder for inference results, by default OUTPUT_FOLDER.

  • end_file – Suffix for the evaluation file, by default None.

  • files – Path to the file for initialization, by default None.

  • rng – Random number generator, by default None.

Returns:

  • u_f – Out-going membership matrix.

  • v_f – In-coming membership matrix.

  • w_f – Affinity tensor.

  • eta_f – Pair interaction coefficient.

  • maxL – Maximum log-likelihood.