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This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

Source code for networkx.generators.directed

# -*- coding: utf-8 -*-
# Copyright (C) 2006-2017 by
#   Aric Hagberg <hagberg@lanl.gov>
#   Dan Schult <dschult@colgate.edu>
#   Pieter Swart <swart@lanl.gov>
# Copyright (C) 2009 by Willem Ligtenberg <W.P.A.Ligtenberg@tue.nl>
# All rights reserved.
# BSD license.
#
# Authors: Aric Hagberg (hagberg@lanl.gov)
#          Willem Ligtenberg (W.P.A.Ligtenberg@tue.nl)
"""
Generators for some directed graphs, including growing network (GN) graphs and
scale-free graphs.

"""
from __future__ import division

from collections import Counter
import random

import networkx as nx
from networkx.generators.classic import empty_graph
from networkx.utils import discrete_sequence
from networkx.utils import weighted_choice

__all__ = ['gn_graph', 'gnc_graph', 'gnr_graph', 'random_k_out_graph',
           'scale_free_graph']


[docs]def gn_graph(n, kernel=None, create_using=None, seed=None): """Return the growing network (GN) digraph with `n` nodes. The GN graph is built by adding nodes one at a time with a link to one previously added node. The target node for the link is chosen with probability based on degree. The default attachment kernel is a linear function of the degree of a node. The graph is always a (directed) tree. Parameters ---------- n : int The number of nodes for the generated graph. kernel : function The attachment kernel. create_using : graph, optional (default DiGraph) Return graph of this type. The instance will be cleared. seed : hashable object, optional The seed for the random number generator. Examples -------- To create the undirected GN graph, use the :meth:`~DiGraph.to_directed` method:: >>> D = nx.gn_graph(10) # the GN graph >>> G = D.to_undirected() # the undirected version To specify an attachment kernel, use the `kernel` keyword argument:: >>> D = nx.gn_graph(10, kernel=lambda x: x ** 1.5) # A_k = k^1.5 References ---------- .. [1] P. L. Krapivsky and S. Redner, Organization of Growing Random Networks, Phys. Rev. E, 63, 066123, 2001. """ if create_using is None: create_using = nx.DiGraph() elif not create_using.is_directed(): raise nx.NetworkXError("Directed Graph required in create_using") if kernel is None: def kernel(x): return x if seed is not None: random.seed(seed) G = empty_graph(1, create_using) if n == 1: return G G.add_edge(1, 0) # get started ds = [1, 1] # degree sequence for source in range(2, n): # compute distribution from kernel and degree dist = [kernel(d) for d in ds] # choose target from discrete distribution target = discrete_sequence(1, distribution=dist)[0] G.add_edge(source, target) ds.append(1) # the source has only one link (degree one) ds[target] += 1 # add one to the target link degree return G
[docs]def gnr_graph(n, p, create_using=None, seed=None): """Return the growing network with redirection (GNR) digraph with `n` nodes and redirection probability `p`. The GNR graph is built by adding nodes one at a time with a link to one previously added node. The previous target node is chosen uniformly at random. With probabiliy `p` the link is instead "redirected" to the successor node of the target. The graph is always a (directed) tree. Parameters ---------- n : int The number of nodes for the generated graph. p : float The redirection probability. create_using : graph, optional (default DiGraph) Return graph of this type. The instance will be cleared. seed : hashable object, optional The seed for the random number generator. Examples -------- To create the undirected GNR graph, use the :meth:`~DiGraph.to_directed` method:: >>> D = nx.gnr_graph(10, 0.5) # the GNR graph >>> G = D.to_undirected() # the undirected version References ---------- .. [1] P. L. Krapivsky and S. Redner, Organization of Growing Random Networks, Phys. Rev. E, 63, 066123, 2001. """ if create_using is None: create_using = nx.DiGraph() elif not create_using.is_directed(): raise nx.NetworkXError("Directed Graph required in create_using") if seed is not None: random.seed(seed) G = empty_graph(1, create_using) if n == 1: return G for source in range(1, n): target = random.randrange(0, source) if random.random() < p and target != 0: target = next(G.successors(target)) G.add_edge(source, target) return G
[docs]def gnc_graph(n, create_using=None, seed=None): """Return the growing network with copying (GNC) digraph with `n` nodes. The GNC graph is built by adding nodes one at a time with a link to one previously added node (chosen uniformly at random) and to all of that node's successors. Parameters ---------- n : int The number of nodes for the generated graph. create_using : graph, optional (default DiGraph) Return graph of this type. The instance will be cleared. seed : hashable object, optional The seed for the random number generator. References ---------- .. [1] P. L. Krapivsky and S. Redner, Network Growth by Copying, Phys. Rev. E, 71, 036118, 2005k.}, """ if create_using is None: create_using = nx.DiGraph() elif not create_using.is_directed(): raise nx.NetworkXError("Directed Graph required in create_using") if seed is not None: random.seed(seed) G = empty_graph(1, create_using) if n == 1: return G for source in range(1, n): target = random.randrange(0, source) for succ in G.successors(target): G.add_edge(source, succ) G.add_edge(source, target) return G
[docs]def scale_free_graph(n, alpha=0.41, beta=0.54, gamma=0.05, delta_in=0.2, delta_out=0, create_using=None, seed=None): """Returns a scale-free directed graph. Parameters ---------- n : integer Number of nodes in graph alpha : float Probability for adding a new node connected to an existing node chosen randomly according to the in-degree distribution. beta : float Probability for adding an edge between two existing nodes. One existing node is chosen randomly according the in-degree distribution and the other chosen randomly according to the out-degree distribution. gamma : float Probability for adding a new node connected to an existing node chosen randomly according to the out-degree distribution. delta_in : float Bias for choosing ndoes from in-degree distribution. delta_out : float Bias for choosing ndoes from out-degree distribution. create_using : graph, optional (default MultiDiGraph) Use this graph instance to start the process (default=3-cycle). seed : integer, optional Seed for random number generator Examples -------- Create a scale-free graph on one hundred nodes:: >>> G = nx.scale_free_graph(100) Notes ----- The sum of `alpha`, `beta`, and `gamma` must be 1. References ---------- .. [1] B. Bollobás, C. Borgs, J. Chayes, and O. Riordan, Directed scale-free graphs, Proceedings of the fourteenth annual ACM-SIAM Symposium on Discrete Algorithms, 132--139, 2003. """ def _choose_node(G, distribution, delta, psum): cumsum = 0.0 # normalization r = random.random() for n, d in distribution: cumsum += (d + delta) / psum if r < cumsum: break return n if create_using is None: # start with 3-cycle G = nx.MultiDiGraph() G.add_edges_from([(0, 1), (1, 2), (2, 0)]) else: # keep existing graph structure? G = create_using if not (G.is_directed() and G.is_multigraph()): raise nx.NetworkXError("MultiDiGraph required in create_using") if alpha <= 0: raise ValueError('alpha must be >= 0.') if beta <= 0: raise ValueError('beta must be >= 0.') if gamma <= 0: raise ValueError('beta must be >= 0.') if alpha + beta + gamma != 1.0: raise ValueError('alpha+beta+gamma must equal 1.') # seed random number generated (uses None as default) random.seed(seed) number_of_edges = G.number_of_edges() while len(G) < n: psum_in = number_of_edges + delta_in * len(G) psum_out = number_of_edges + delta_out * len(G) r = random.random() # random choice in alpha,beta,gamma ranges if r < alpha: # alpha # add new node v v = len(G) # choose w according to in-degree and delta_in w = _choose_node(G, G.in_degree(), delta_in, psum_in) elif r < alpha + beta: # beta # choose v according to out-degree and delta_out v = _choose_node(G, G.out_degree(), delta_out, psum_out) # choose w according to in-degree and delta_in w = _choose_node(G, G.in_degree(), delta_in, psum_in) else: # gamma # choose v according to out-degree and delta_out v = _choose_node(G, G.out_degree(), delta_out, psum_out) # add new node w w = len(G) G.add_edge(v, w) number_of_edges += 1 return G
def random_uniform_k_out_graph(n, k, self_loops=True, with_replacement=True, seed=None): """Returns a random `k`-out graph with uniform attachment. A random `k`-out graph with uniform attachment is a multidigraph generated by the following algorithm. For each node *u*, choose `k` nodes *v* uniformly at random (with replacement). Add a directed edge joining *u* to *v*. Parameters ---------- n : int The number of nodes in the returned graph. k : int The out-degree of each node in the returned graph. self_loops : bool If True, self-loops are allowed when generating the graph. with_replacement : bool If True, neighbors are chosen with replacement and the returned graph will be a directed multigraph. Otherwise, neighbors are chosen without replacement and the returned graph will be a directed graph. seed: int If provided, this is used as the seed for the random number generator. Returns ------- NetworkX graph A `k`-out-regular directed graph generated according to the above algorithm. It will be a multigraph if and only if `with_replacement` is True. Raises ------ ValueError If `with_replacement` is False and `k` is greater than `n`. See also -------- random_k_out_graph Notes ----- The return digraph or multidigraph may not be strongly connected, or even weakly connected. If `with_replacement` is True, this function is similar to :func:`random_k_out_graph`, if that function had parameter `alpha` set to positive infinity. """ random.seed(seed) if with_replacement: create_using = nx.MultiDiGraph() def sample(v, nodes): if not self_loops: nodes = nodes - {v} return (random.choice(list(nodes)) for i in range(k)) else: create_using = nx.DiGraph() def sample(v, nodes): if not self_loops: nodes = nodes - {v} return random.sample(nodes, k) G = nx.empty_graph(n, create_using=create_using) nodes = set(G) for u in G: G.add_edges_from((u, v) for v in sample(u, nodes)) return G
[docs]def random_k_out_graph(n, k, alpha, self_loops=True, seed=None): """Returns a random `k`-out graph with preferential attachment. A random `k`-out graph with preferential attachment is a multidigraph generated by the following algorithm. 1. Begin with an empty digraph, and initially set each node to have weight `alpha`. 2. Choose a node `u` with out-degree less than `k` uniformly at random. 3. Choose a node `v` from with probability proportional to its weight. 4. Add a directed edge from `u` to `v`, and increase the weight of `v` by one. 5. If each node has out-degree `k`, halt, otherwise repeat from step 2. For more information on this model of random graph, see [1]. Parameters ---------- n : int The number of nodes in the returned graph. k : int The out-degree of each node in the returned graph. alpha : float A positive :class:`float` representing the initial weight of each vertex. A higher number means that in step 3 above, nodes will be chosen more like a true uniformly random sample, and a lower number means that nodes are more likely to be chosen as their in-degree increases. If this parameter is not positive, a :exc:`ValueError` is raised. self_loops : bool If True, self-loops are allowed when generating the graph. seed: int If provided, this is used as the seed for the random number generator. Returns ------- :class:`~networkx.classes.MultiDiGraph` A `k`-out-regular multidigraph generated according to the above algorithm. Raises ------ ValueError If `alpha` is not positive. Notes ----- The returned multidigraph may not be strongly connected, or even weakly connected. References ---------- [1]: Peterson, Nicholas R., and Boris Pittel. "Distance between two random `k`-out digraphs, with and without preferential attachment." arXiv preprint arXiv:1311.5961 (2013). <https://arxiv.org/abs/1311.5961> """ if alpha < 0: raise ValueError('alpha must be positive') random.seed(seed) G = nx.empty_graph(n, create_using=nx.MultiDiGraph()) weights = Counter({v: alpha for v in G}) for i in range(k * n): u = random.choice([v for v, d in G.out_degree() if d < k]) # If self-loops are not allowed, make the source node `u` have # weight zero. if not self_loops: adjustment = Counter({u: weights[u]}) else: adjustment = Counter() v = weighted_choice(weights - adjustment) G.add_edge(u, v) weights[v] += 1 return G