# -*- coding: utf-8 -*-
# Copyright (C) 2015-2018 Aitor Almeida
# All rights reserved.
# BSD license.
#
# Author: Aitor Almeida <aitoralmeida@gmail.com>
"""
Label propagation community detection algorithms.
"""
from collections import Counter
import random
import networkx as nx
from networkx.utils import groups
from networkx.utils.decorators import not_implemented_for
__all__ = ['label_propagation_communities', 'asyn_lpa_communities']
[docs]def asyn_lpa_communities(G, weight=None):
"""Returns communities in `G` as detected by asynchronous label
propagation.
The asynchronous label propagation algorithm is described in
[1]_. The algorithm is probabilistic and the found communities may
vary on different executions.
The algorithm proceeds as follows. After initializing each node with
a unique label, the algorithm repeatedly sets the label of a node to
be the label that appears most frequently among that nodes
neighbors. The algorithm halts when each node has the label that
appears most frequently among its neighbors. The algorithm is
asynchronous because each node is updated without waiting for
updates on the remaining nodes.
This generalized version of the algorithm in [1]_ accepts edge
weights.
Parameters
----------
G : Graph
weight : string
The edge attribute representing the weight of an edge.
If None, each edge is assumed to have weight one. In this
algorithm, the weight of an edge is used in determining the
frequency with which a label appears among the neighbors of a
node: a higher weight means the label appears more often.
Returns
-------
communities : iterable
Iterable of communities given as sets of nodes.
Notes
------
Edge weight attributes must be numerical.
References
----------
.. [1] Raghavan, Usha Nandini, Réka Albert, and Soundar Kumara. "Near
linear time algorithm to detect community structures in large-scale
networks." Physical Review E 76.3 (2007): 036106.
"""
labels = {n: i for i, n in enumerate(G)}
cont = True
while cont:
cont = False
nodes = list(G)
random.shuffle(nodes)
# Calculate the label for each node
for node in nodes:
if len(G[node]) < 1:
continue
# Get label frequencies. Depending on the order they are processed
# in some nodes with be in t and others in t-1, making the
# algorithm asynchronous.
label_freq = Counter()
for v in G[node]:
label_freq.update({labels[v]: G.edges[v, node][weight]
if weight else 1})
# Choose the label with the highest frecuency. If more than 1 label
# has the highest frecuency choose one randomly.
max_freq = max(label_freq.values())
best_labels = [label for label, freq in label_freq.items()
if freq == max_freq]
new_label = random.choice(best_labels)
labels[node] = new_label
# Continue until all nodes have a label that is better than other
# neighbour labels (only one label has max_freq for each node).
cont = cont or len(best_labels) > 1
# TODO In Python 3.3 or later, this should be `yield from ...`.
return iter(groups(labels).values())
[docs]@not_implemented_for('directed')
def label_propagation_communities(G):
"""Generates community sets determined by label propagation
Finds communities in `G` using a semi-synchronous label propagation
method[1]_. This method combines the advantages of both the synchronous
and asynchronous models. Not implemented for directed graphs.
Parameters
----------
G : graph
An undirected NetworkX graph.
Yields
------
communities : generator
Yields sets of the nodes in each community.
Raises
------
NetworkXNotImplemented
If the graph is directed
References
----------
.. [1] Cordasco, G., & Gargano, L. (2010, December). Community detection
via semi-synchronous label propagation algorithms. In Business
Applications of Social Network Analysis (BASNA), 2010 IEEE International
Workshop on (pp. 1-8). IEEE.
"""
coloring = _color_network(G)
# Create a unique label for each node in the graph
labeling = {v: k for k, v in enumerate(G)}
while not _labeling_complete(labeling, G):
# Update the labels of every node with the same color.
for color, nodes in coloring.items():
for n in nodes:
_update_label(n, labeling, G)
for label in set(labeling.values()):
yield set((x for x in labeling if labeling[x] == label))
def _color_network(G):
"""Colors the network so that neighboring nodes all have distinct colors.
Returns a dict keyed by color to a set of nodes with that color.
"""
coloring = dict() # color => set(node)
colors = nx.coloring.greedy_color(G)
for node, color in colors.items():
if color in coloring:
coloring[color].add(node)
else:
coloring[color] = set([node])
return coloring
def _labeling_complete(labeling, G):
"""Determines whether or not LPA is done.
Label propagation is complete when all nodes have a label that is
in the set of highest frequency labels amongst its neighbors.
Nodes with no neighbors are considered complete.
"""
return all(labeling[v] in _most_frequent_labels(v, labeling, G)
for v in G if len(G[v]) > 0)
def _most_frequent_labels(node, labeling, G):
"""Returns a set of all labels with maximum frequency in `labeling`.
Input `labeling` should be a dict keyed by node to labels.
"""
if not G[node]:
# Nodes with no neighbors are themselves a community and are labeled
# accordingly, hence the immediate if statement.
return {labeling[node]}
# Compute the frequencies of all neighbours of node
freqs = Counter(labeling[q] for q in G[node])
max_freq = max(freqs.values())
return {label for label, freq in freqs.items() if freq == max_freq}
def _update_label(node, labeling, G):
"""Updates the label of a node using the Prec-Max tie breaking algorithm
The algorithm is explained in: 'Community Detection via Semi-Synchronous
Label Propagation Algorithms' Cordasco and Gargano, 2011
"""
high_labels = _most_frequent_labels(node, labeling, G)
if len(high_labels) == 1:
labeling[node] = high_labels.pop()
elif len(high_labels) > 1:
# Prec-Max
if labeling[node] not in high_labels:
labeling[node] = max(high_labels)