#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on December 29, 2020
@author: Thomas Bonald <bonald@enst.fr>
"""
import numpy as np
from scipy import sparse
[docs]def get_neighbors(input_matrix: sparse.csr_matrix, node: int, transpose: bool = False) -> np.ndarray:
"""Get the neighbors of a node.
If the graph is directed, returns the vector of successors. Set ``transpose=True``
to get the predecessors.
For a biadjacency matrix, returns the neighbors of a row node. Set ``transpose=True``
to get the neighbors of a column node.
Parameters
----------
input_matrix : sparse.csr_matrix
Adjacency or biadjacency matrix.
node : int
Target node.
transpose :
If ``True``, transpose the input matrix.
Returns
-------
neighbors : np.ndarray
Array of neighbors of the target node.
Example
-------
>>> from sknetwork.data import house
>>> adjacency = house()
>>> get_neighbors(adjacency, node=0)
array([1, 4], dtype=int32)
"""
if transpose:
matrix = sparse.csr_matrix(input_matrix.T)
else:
matrix = input_matrix
neighbors = matrix.indices[matrix.indptr[node]: matrix.indptr[node + 1]]
return neighbors
[docs]def get_degrees(input_matrix: sparse.csr_matrix, transpose: bool = False) -> np.ndarray:
"""Get the vector of degrees of a graph.
If the graph is directed, returns the out-degrees (number of successors). Set ``transpose=True``
to get the in-degrees (number of predecessors).
For a biadjacency matrix, returns the degrees of rows. Set ``transpose=True``
to get the degrees of columns.
Parameters
----------
input_matrix : sparse.csr_matrix
Adjacency or biadjacency matrix.
transpose :
If ``True``, transpose the input matrix.
Returns
-------
degrees : np.ndarray
Array of degrees.
Example
-------
>>> from sknetwork.data import house
>>> adjacency = house()
>>> get_degrees(adjacency)
array([2, 3, 2, 2, 3], dtype=int32)
"""
if transpose:
matrix = sparse.csr_matrix(input_matrix.T)
else:
matrix = input_matrix
degrees = matrix.indptr[1:] - matrix.indptr[:-1]
return degrees
[docs]def get_weights(input_matrix: sparse.csr_matrix, transpose: bool = False) -> np.ndarray:
"""Get the vector of weights of the nodes of a graph. If the graph is not weighted, return the vector of degrees.
If the graph is directed, returns the out-weights (total weight of outgoing links). Set ``transpose=True``
to get the in-weights (total weight of incoming links).
For a biadjacency matrix, returns the weights of rows. Set ``transpose=True``
to get the weights of columns.
Parameters
----------
input_matrix : sparse.csr_matrix
Adjacency or biadjacency matrix.
transpose :
If ``True``, transpose the input matrix.
Returns
-------
weights : np.ndarray
Array of weights.
Example
-------
>>> from sknetwork.data import house
>>> adjacency = house()
>>> get_weights(adjacency)
array([2., 3., 2., 2., 3.])
"""
if transpose:
matrix = sparse.csr_matrix(input_matrix.T)
else:
matrix = input_matrix
weights = matrix.dot(np.ones(matrix.shape[1]))
return weights