API#

`probinet.evaluation`	Output Module
`probinet.evaluation.community_detection`	Functions for evaluating community detection.
`probinet.evaluation.covariate_prediction`	Module for extracting true labels and predicting labels.
`probinet.evaluation.expectation_computation`	Functions for computing expectations and related metrics.
`probinet.evaluation.likelihood`	This module provides functions for computing the log-likelihood and pseudo log-likelihood of the models, as well as other related calculations.
`probinet.evaluation.link_prediction`	Functions for evaluating link prediction.
`probinet.input`	Input Module
`probinet.input.loader`	Functions for handling the data.
`probinet.input.preprocessing`	It provides functions for preprocessing and constructing adjacency tensors from NetworkX graphs.
`probinet.input.stats`	It is designed to compute and print statistical information about NetworkX graphs.
`probinet.main`	Main script to run the algorithms.
`probinet.model_selection`	Model Selection Module
`probinet.model_selection.acd_cross_validation`	This module contains the ACDCrossValidation class, which is used to perform cross-validation of the ACD algorithm.
`probinet.model_selection.crep_cross_validation`	This module contains the CRepCrossValidation class, which is used for cross-validation of the CRep algorithm.
`probinet.model_selection.cross_validation`	Main function to implement cross-validation given a number of communities.
`probinet.model_selection.dyncrep_cross_validation`	This module contains the DynCRepCrossValidation class, which is used for cross-validation of the DynCRep algorithm.
`probinet.model_selection.jointcrep_cross_validation`	This module contains the JointCRepCrossValidation class, which is used for cross-validation of the JointCRep algorithm.
`probinet.model_selection.main`	Main module for running cross-validation for different algorithms.
`probinet.model_selection.masking`	This module provides functions for shuffling indices and extracting masks for selecting the held-out set in the adjacency tensor and design matrix.
`probinet.model_selection.mtcov_cross_validation`	This module contains the MTCOVCrossValidation class, which is used for cross-validation of the MTCOV algorithm.
`probinet.model_selection.parameter_search`	This module defines the grid of parameters to be tested for models selection.
`probinet.models`	Model Module
`probinet.models.acd`	Class definition of ACD, the algorithm to perform inference in networks with anomaly.
`probinet.models.base`	Base classes for the models classes.
`probinet.models.classes`	This module contains the definition of namedtuple classes.
`probinet.models.constants`	This file contains the constants used in the models.
`probinet.models.crep`	Class definition of CRep, the algorithm to perform inference in networks with reciprocity.
`probinet.models.dyncrep`	Class definition of DynCRep, the algorithm to perform inference in temporal networks [SCDB22].
`probinet.models.jointcrep`	Class definition of JointCRep, the algorithm to perform inference in networks with reciprocity.
`probinet.models.mtcov`	Class definition of MTCOV, the generative algorithm that incorporates both the topology of interactions and node attributes to extract overlapping communities in directed and undirected multilayer networks [CPDB20].
`probinet.synthetic`	Synthetic Module
`probinet.synthetic.anomaly`	Class for generation and management of synthetic networks with anomalies
`probinet.synthetic.base`	Base classes for synthetic network generation.
`probinet.synthetic.dynamic`	Class definition of the reciprocity generative models with the member functions required.
`probinet.synthetic.multilayer`	Code to generate multilayer networks with non-negative and discrete weights, and whose nodes are associated with one categorical attribute.
`probinet.synthetic.reciprocity`	Class definition of the reciprocity generative models with the member functions required.
`probinet.utils`	Utils Module
`probinet.utils.matrix_operations`	This module contains functions that perform matrix operations, such as the Khatri-Rao product.
`probinet.utils.tools`	This module contains utility functions for data manipulation and file I/O.
`probinet.version`	Version file.
`probinet.visualization`	Visualization Module
`probinet.visualization.plot`	It provides a set of plotting functions for visualizing the results of the generative models.