Source code for sknetwork.clustering.louvain

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
Created in November 2018
@author: Nathan de Lara <>
@author: Quentin Lutz <>
@author: Thomas Bonald <>
from typing import Union, Optional

import numpy as np
from scipy import sparse

from sknetwork.clustering.base import BaseClustering
from sknetwork.clustering.louvain_core import fit_core
from sknetwork.clustering.postprocess import reindex_labels
from sknetwork.utils.check import check_random_state, get_probs
from sknetwork.utils.format import check_format, get_adjacency, directed2undirected
from sknetwork.utils.membership import get_membership
from sknetwork.log import Log

[docs]class Louvain(BaseClustering, Log): """Louvain algorithm for clustering graphs by maximization of modularity. For bipartite graphs, the algorithm maximizes Barber's modularity by default. Parameters ---------- resolution : Resolution parameter. modularity : str Which objective function to maximize. Can be ``'Dugue'``, ``'Newman'`` or ``'Potts'`` (default = ``'dugue'``). tol_optimization : Minimum increase in the objective function to enter a new optimization pass. tol_aggregation : Minimum increase in the objective function to enter a new aggregation pass. n_aggregations : Maximum number of aggregations. A negative value is interpreted as no limit. shuffle_nodes : Enables node shuffling before optimization. sort_clusters : If ``True``, sort labels in decreasing order of cluster size. return_probs : If ``True``, return the probability distribution over clusters (soft clustering). return_aggregate : If ``True``, return the adjacency matrix of the graph between clusters. random_state : Random number generator or random seed. If None, numpy.random is used. verbose : Verbose mode. Attributes ---------- labels_ : np.ndarray, shape (n_labels,) Label of each node. probs_ : sparse.csr_matrix, shape (n_row, n_labels) Probability distribution over labels. labels_row_, labels_col_ : np.ndarray Labels of rows and columns, for bipartite graphs. probs_row_, probs_col_ : sparse.csr_matrix, shape (n_row, n_labels) Probability distributions over labels for rows and columns (for bipartite graphs). aggregate_ : sparse.csr_matrix Aggregate adjacency matrix or biadjacency matrix between clusters. Example ------- >>> from sknetwork.clustering import Louvain >>> from import karate_club >>> louvain = Louvain() >>> adjacency = karate_club() >>> labels = louvain.fit_predict(adjacency) >>> len(set(labels)) 4 References ---------- * Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). `Fast unfolding of communities in large networks. <>`_ Journal of statistical mechanics: theory and experiment, 2008. * Dugué, N., & Perez, A. (2015). `Directed Louvain: maximizing modularity in directed networks <>`_ (Doctoral dissertation, Université d'Orléans). * Barber, M. J. (2007). `Modularity and community detection in bipartite networks <>`_ Physical Review E, 76(6). """ def __init__(self, resolution: float = 1, modularity: str = 'dugue', tol_optimization: float = 1e-3, tol_aggregation: float = 1e-3, n_aggregations: int = -1, shuffle_nodes: bool = False, sort_clusters: bool = True, return_probs: bool = True, return_aggregate: bool = True, random_state: Optional[Union[np.random.RandomState, int]] = None, verbose: bool = False): super(Louvain, self).__init__(sort_clusters=sort_clusters, return_probs=return_probs, return_aggregate=return_aggregate) Log.__init__(self, verbose) self.labels_ = None self.resolution = resolution self.modularity = modularity.lower() self.tol = tol_optimization self.tol_aggregation = tol_aggregation self.n_aggregations = n_aggregations self.shuffle_nodes = shuffle_nodes self.random_state = check_random_state(random_state) self.bipartite = None def _optimize(self, adjacency_norm, probs_ou, probs_in): """One local optimization pass of the Louvain algorithm Parameters ---------- adjacency_norm : the norm of the adjacency probs_ou : the array of degrees of the adjacency probs_in : the array of degrees of the transpose of the adjacency Returns ------- labels : the communities of each node after optimization pass_increase : the increase in modularity gained after optimization """ node_probs_in = probs_in.astype(np.float32) node_probs_ou = probs_ou.astype(np.float32) adjacency = 0.5 * directed2undirected(adjacency_norm) self_loops = adjacency.diagonal().astype(np.float32) indptr: np.ndarray = adjacency.indptr indices: np.ndarray = adjacency.indices data: np.ndarray = return fit_core(self.resolution, self.tol, node_probs_ou, node_probs_in, self_loops, data, indices, indptr) @staticmethod def _aggregate(adjacency_norm, probs_out, probs_in, membership: Union[sparse.csr_matrix, np.ndarray]): """Aggregate nodes belonging to the same cluster. Parameters ---------- adjacency_norm : the norm of the adjacency probs_out : the array of degrees of the adjacency probs_in : the array of degrees of the transpose of the adjacency membership : membership matrix (rows). Returns ------- Aggregate graph. """ adjacency_norm = ( probs_in = np.array( probs_out = np.array( return adjacency_norm, probs_out, probs_in
[docs] def fit(self, input_matrix: Union[sparse.csr_matrix, np.ndarray], force_bipartite: bool = False) -> 'Louvain': """Fit algorithm to data. Parameters ---------- input_matrix : Adjacency matrix or biadjacency matrix of the graph. force_bipartite : If ``True``, force the input matrix to be considered as a biadjacency matrix even if square. Returns ------- self : :class:`Louvain` """ self._init_vars() input_matrix = check_format(input_matrix) if self.modularity == 'dugue': adjacency, self.bipartite = get_adjacency(input_matrix, force_directed=True, force_bipartite=force_bipartite) else: adjacency, self.bipartite = get_adjacency(input_matrix, force_bipartite=force_bipartite) n = adjacency.shape[0] index = np.arange(n) if self.shuffle_nodes: index = self.random_state.permutation(index) adjacency = adjacency[index][:, index] if self.modularity == 'potts': probs_out = get_probs('uniform', adjacency) probs_in = probs_out.copy() elif self.modularity == 'newman': probs_out = get_probs('degree', adjacency) probs_in = probs_out.copy() elif self.modularity == 'dugue': probs_out = get_probs('degree', adjacency) probs_in = get_probs('degree', adjacency.T) else: raise ValueError('Unknown modularity function.') adjacency_cluster = adjacency / membership = sparse.identity(n, format='csr') increase = True count_aggregations = 0 self.print_log("Starting with", n, "nodes.") while increase: count_aggregations += 1 labels_cluster, pass_increase = self._optimize(adjacency_cluster, probs_out, probs_in) _, labels_cluster = np.unique(labels_cluster, return_inverse=True) if pass_increase <= self.tol_aggregation: increase = False else: membership_cluster = get_membership(labels_cluster) membership = adjacency_cluster, probs_out, probs_in = self._aggregate(adjacency_cluster, probs_out, probs_in, membership_cluster) n = adjacency_cluster.shape[0] if n == 1: break self.print_log("Aggregation", count_aggregations, "completed with", n, "clusters and ", pass_increase, "increment.") if count_aggregations == self.n_aggregations: break if self.sort_clusters: labels = reindex_labels(membership.indices) else: labels = membership.indices if self.shuffle_nodes: reverse = np.empty(index.size, index.dtype) reverse[index] = np.arange(index.size) labels = labels[reverse] self.labels_ = labels if self.bipartite: self._split_vars(input_matrix.shape) self._secondary_outputs(input_matrix) return self