Source code for sknetwork.utils.co_neighbor

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
Created on October 2019
@author: Nathan de Lara <>
from typing import Union

import numpy as np
from scipy import sparse

from sknetwork.embedding.svd import SVD, GSVD
from sknetwork.linalg.normalization import normalize
from sknetwork.utils.check import check_format
from sknetwork.utils.knn import KNNDense

[docs]def co_neighbor_graph(adjacency: Union[sparse.csr_matrix, np.ndarray], normalized: bool = True, method='knn', n_neighbors: int = 5, n_components: int = 8) -> sparse.csr_matrix: """Compute the co-neighborhood adjacency. * Graphs * Digraphs * Bigraphs :math:`\\tilde{A} = AF^{-1}A^T`, where F is a weight matrix. Parameters ---------- adjacency: Adjacency of the input graph. normalized: If ``True``, F is the diagonal in-degree matrix :math:`F = \\text{diag}(A^T1)`. Otherwise, F is the identity matrix. method: Either ``'exact'`` or ``'knn'``. If 'exact' the output is computed with matrix multiplication. However, the density can be much higher than in the input graph and this can trigger Memory errors. If ``'knn'``, the co-neighborhood is approximated through KNNDense-search in an appropriate spectral embedding space. n_neighbors: Number of neighbors for the KNNDense search. Only useful if ``method='knn'``. n_components: Dimension of the embedding space. Only useful if ``method='knn'``. Returns ------- adjacency : sparse.csr_matrix Adjacency of the co-neighborhood. """ adjacency = check_format(adjacency).astype(float) method = method.lower() if method == 'exact': if normalized: forward = normalize(adjacency.T).tocsr() else: forward = adjacency.T return elif method == 'knn': if normalized: algo = GSVD(n_components, regularization=None) else: algo = SVD(n_components, regularization=None) embedding = algo.fit_transform(adjacency) knn = KNNDense(n_neighbors, undirected=True) return knn.adjacency_ else: raise ValueError('method must be "exact" or "knn".')