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Source code for networkx.algorithms.community.kernighan_lin

# -*- coding: utf-8 -*-
#
# kernighan_lin.py - Kernighan–Lin bipartition algorithm
#
# Copyright 2011 Ben Edwards <bedwards@cs.unm.edu>.
# Copyright 2011 Aric Hagberg <hagberg@lanl.gov>.
# Copyright 2015 NetworkX developers.
#
# This file is part of NetworkX.
#
# NetworkX is distributed under a BSD license; see LICENSE.txt for more
# information.
"""Functions for computing the Kernighan–Lin bipartition algorithm."""

from collections import defaultdict
from itertools import islice
from operator import itemgetter

import networkx as nx
from networkx.utils import not_implemented_for, py_random_state
from networkx.algorithms.community.community_utils import is_partition

__all__ = ['kernighan_lin_bisection']


def _compute_delta(G, A, B, weight):
    # helper to compute initial swap deltas for a pass
    delta = defaultdict(float)
    for u, v, d in G.edges(data=True):
        w = d.get(weight, 1)
        if u in A:
            if v in A:
                delta[u] -= w
                delta[v] -= w
            elif v in B:
                delta[u] += w
                delta[v] += w
        elif u in B:
            if v in A:
                delta[u] += w
                delta[v] += w
            elif v in B:
                delta[u] -= w
                delta[v] -= w
    return delta


def _update_delta(delta, G, A, B, u, v, weight):
    # helper to update swap deltas during single pass
    for _, nbr, d in G.edges(u, data=True):
        w = d.get(weight, 1)
        if nbr in A:
            delta[nbr] += 2 * w
        if nbr in B:
            delta[nbr] -= 2 * w
    for _, nbr, d in G.edges(v, data=True):
        w = d.get(weight, 1)
        if nbr in A:
            delta[nbr] -= 2 * w
        if nbr in B:
            delta[nbr] += 2 * w
    return delta


def _kernighan_lin_pass(G, A, B, weight):
    # do a single iteration of Kernighan–Lin algorithm
    # returns list of  (g_i,u_i,v_i) for i node pairs u_i,v_i
    multigraph = G.is_multigraph()
    delta = _compute_delta(G, A, B, weight)
    swapped = set()
    gains = []
    while len(swapped) < len(G):
        gain = []
        for u in A - swapped:
            for v in B - swapped:
                try:
                    if multigraph:
                        w = sum(d.get(weight, 1) for d in G[u][v].values())
                    else:
                        w = G[u][v].get(weight, 1)
                except KeyError:
                    w = 0
                gain.append((delta[u] + delta[v] - 2 * w, u, v))
        if len(gain) == 0:
            break
        maxg, u, v = max(gain, key=itemgetter(0))
        swapped |= {u, v}
        gains.append((maxg, u, v))
        delta = _update_delta(delta, G, A - swapped, B - swapped, u, v, weight)
    return gains


[docs]@py_random_state(4) @not_implemented_for('directed') def kernighan_lin_bisection(G, partition=None, max_iter=10, weight='weight', seed=None): """Partition a graph into two blocks using the Kernighan–Lin algorithm. This algorithm paritions a network into two sets by iteratively swapping pairs of nodes to reduce the edge cut between the two sets. Parameters ---------- G : graph partition : tuple Pair of iterables containing an initial partition. If not specified, a random balanced partition is used. max_iter : int Maximum number of times to attempt swaps to find an improvemement before giving up. weight : key Edge data key to use as weight. If None, the weights are all set to one. seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness<randomness>`. Only used if partition is None Returns ------- partition : tuple A pair of sets of nodes representing the bipartition. Raises ------- NetworkXError If partition is not a valid partition of the nodes of the graph. References ---------- .. [1] Kernighan, B. W.; Lin, Shen (1970). "An efficient heuristic procedure for partitioning graphs." *Bell Systems Technical Journal* 49: 291--307. Oxford University Press 2011. """ # If no partition is provided, split the nodes randomly into a # balanced partition. if partition is None: nodes = list(G) seed.shuffle(nodes) h = len(nodes) // 2 partition = (nodes[:h], nodes[h:]) # Make a copy of the partition as a pair of sets. try: A, B = set(partition[0]), set(partition[1]) except: raise ValueError('partition must be two sets') if not is_partition(G, (A, B)): raise nx.NetworkXError('partition invalid') for i in range(max_iter): # `gains` is a list of triples of the form (g, u, v) for each # node pair (u, v), where `g` is the gain of that node pair. gains = _kernighan_lin_pass(G, A, B, weight) csum = list(nx.utils.accumulate(g for g, u, v in gains)) max_cgain = max(csum) if max_cgain <= 0: break # Get the node pairs up to the index of the maximum cumulative # gain, and collect each `u` into `anodes` and each `v` into # `bnodes`, for each pair `(u, v)`. index = csum.index(max_cgain) nodesets = islice(zip(*gains[:index + 1]), 1, 3) anodes, bnodes = (set(s) for s in nodesets) A |= bnodes A -= anodes B |= anodes B -= bnodes return A, B