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# Source code for networkx.generators.joint_degree_seq

#    Minas Gjoka
#
# Author:  Minas Gjoka (minas.gjoka@gmail.com)
"""Generate graphs with a given joint degree """
from __future__ import division

import networkx as nx
from networkx.utils import py_random_state

__all__ = ['is_valid_joint_degree',
'joint_degree_graph']

[docs]def is_valid_joint_degree(joint_degrees): """ Checks whether the given joint degree dictionary is realizable as a simple graph. A *joint degree dictionary* is a dictionary of dictionaries, in which entry joint_degrees[k][l] is an integer representing the number of edges joining nodes of degree *k* with nodes of degree *l*. Such a dictionary is realizable as a simple graph if and only if the following conditions are satisfied. - each entry must be an integer, - the total number of nodes of degree *k*, computed by sum(joint_degrees[k].values()) / k, must be an integer, - the total number of edges joining nodes of degree *k* with nodes of degree *l* cannot exceed the total number of possible edges, - each diagonal entry joint_degrees[k][k] must be even (this is a convention assumed by the :func:joint_degree_graph function). Parameters ---------- joint_degrees : dictionary of dictionary of integers A joint degree dictionary in which entry joint_degrees[k][l] is the number of edges joining nodes of degree *k* with nodes of degree *l*. Returns ------- bool Whether the given joint degree dictionary is realizable as a simple graph. References ---------- .. [1] M. Gjoka, M. Kurant, A. Markopoulou, "2.5K Graphs: from Sampling to Generation", IEEE Infocom, 2013. .. [2] I. Stanton, A. Pinar, "Constructing and sampling graphs with a prescribed joint degree distribution", Journal of Experimental Algorithmics, 2012. """ degree_count = {} for k in joint_degrees: if k > 0: k_size = sum(joint_degrees[k].values()) / k if not k_size.is_integer(): return False degree_count[k] = k_size for k in joint_degrees: for l in joint_degrees[k]: if not float(joint_degrees[k][l]).is_integer(): return False if (k != l) and (joint_degrees[k][l] > degree_count[k] * degree_count[l]): return False elif k == l: if joint_degrees[k][k] > degree_count[k] * (degree_count[k] - 1): return False if joint_degrees[k][k] % 2 != 0: return False # if all above conditions have been satisfied then the input # joint degree is realizable as a simple graph. return True
def _neighbor_switch(G, w, unsat, h_node_residual, avoid_node_id=None): """ Releases one free stub for saturated node w, while preserving joint degree in graph G. Parameters ---------- G : NetworkX graph Graph in which the neighbor switch will take place. w : integer Node id for which we will execute this neighbor switch. unsat : set of integers Set of unsaturated node ids that have the same degree as w. h_node_residual: dictionary of integers Keeps track of the remaining stubs for a given node. avoid_node_id: integer Node id to avoid when selecting w_prime. Notes ----- First, it selects *w_prime*, an unsaturated node that has the same degree as w. Second, it selects *switch_node*, a neighbor node of w that is not connected to *w_prime*. Then it executes an edge swap i.e. removes (w,*switch_node*) and adds (*w_prime*,*switch_node*). Gjoka et. al. [1] prove that such an edge swap is always possible. References ---------- .. [1] M. Gjoka, B. Tillman, A. Markopoulou, "Construction of Simple Graphs with a Target Joint Degree Matrix and Beyond", IEEE Infocom, '15 """ if (avoid_node_id is None) or (h_node_residual[avoid_node_id] > 1): # select unsatured node w_prime that has the same degree as w w_prime = next(iter(unsat)) else: # assume that the node pair (v,w) has been selected for connection. if # - neighbor_switch is called for node w, # - nodes v and w have the same degree, # - node v=avoid_node_id has only one stub left, # then prevent v=avoid_node_id from being selected as w_prime. iter_var = iter(unsat) while True: w_prime = next(iter_var) if w_prime != avoid_node_id: break # select switch_node, a neighbor of w, that is not connected to w_prime w_prime_neighbs = G[w_prime] # slightly faster declaring this variable for v in G[w]: if (v not in w_prime_neighbs) and (v != w_prime): switch_node = v break # remove edge (w,switch_node), add edge (w_prime,switch_node) and update # data structures G.remove_edge(w, switch_node) G.add_edge(w_prime, switch_node) h_node_residual[w] += 1 h_node_residual[w_prime] -= 1 if h_node_residual[w_prime] == 0: unsat.remove(w_prime)
[docs]@py_random_state(1) def joint_degree_graph(joint_degrees, seed=None): """ Generates a random simple graph with the given joint degree dictionary. Parameters ---------- joint_degrees : dictionary of dictionary of integers A joint degree dictionary in which entry joint_degrees[k][l] is the number of edges joining nodes of degree *k* with nodes of degree *l*. seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:Randomness<randomness>. Returns ------- G : Graph A graph with the specified joint degree dictionary. Raises ------ NetworkXError If *joint_degrees* dictionary is not realizable. Notes ----- In each iteration of the "while loop" the algorithm picks two disconnected nodes *v* and *w*, of degree *k* and *l* correspondingly, for which joint_degrees[k][l] has not reached its target yet. It then adds edge (*v*, *w*) and increases the number of edges in graph G by one. The intelligence of the algorithm lies in the fact that it is always possible to add an edge between such disconnected nodes *v* and *w*, even if one or both nodes do not have free stubs. That is made possible by executing a "neighbor switch", an edge rewiring move that releases a free stub while keeping the joint degree of G the same. The algorithm continues for E (number of edges) iterations of the "while loop", at the which point all entries of the given joint_degrees[k][l] have reached their target values and the construction is complete. References ---------- .. [1] M. Gjoka, B. Tillman, A. Markopoulou, "Construction of Simple Graphs with a Target Joint Degree Matrix and Beyond", IEEE Infocom, '15. Examples -------- >>> import networkx as nx >>> joint_degrees = {1: {4: 1}, ... 2: {2: 2, 3: 2, 4: 2}, ... 3: {2: 2, 4: 1}, ... 4: {1: 1, 2: 2, 3: 1}} >>> G=nx.joint_degree_graph(joint_degrees) >>> """ if not is_valid_joint_degree(joint_degrees): msg = 'Input joint degree dict not realizable as a simple graph' raise nx.NetworkXError(msg) # compute degree count from joint_degrees degree_count = {k: sum(l.values()) // k for k, l in joint_degrees.items() if k > 0} # start with empty N-node graph N = sum(degree_count.values()) G = nx.empty_graph(N) # for a given degree group, keep the list of all node ids h_degree_nodelist = {} # for a given node, keep track of the remaining stubs h_node_residual = {} # populate h_degree_nodelist and h_node_residual nodeid = 0 for degree, num_nodes in degree_count.items(): h_degree_nodelist[degree] = range(nodeid, nodeid + num_nodes) for v in h_degree_nodelist[degree]: h_node_residual[v] = degree nodeid += int(num_nodes) # iterate over every degree pair (k,l) and add the number of edges given # for each pair for k in joint_degrees: for l in joint_degrees[k]: # n_edges_add is the number of edges to add for the # degree pair (k,l) n_edges_add = joint_degrees[k][l] if (n_edges_add > 0) and (k >= l): # number of nodes with degree k and l k_size = degree_count[k] l_size = degree_count[l] # k_nodes and l_nodes consist of all nodes of degree k and l k_nodes = h_degree_nodelist[k] l_nodes = h_degree_nodelist[l] # k_unsat and l_unsat consist of nodes of degree k and l that # are unsaturated i.e. nodes that have at least 1 available stub k_unsat = set(v for v in k_nodes if h_node_residual[v] > 0) if k != l: l_unsat = set(w for w in l_nodes if h_node_residual[w] > 0) else: l_unsat = k_unsat n_edges_add = joint_degrees[k][l] // 2 while n_edges_add > 0: # randomly pick nodes v and w that have degrees k and l v = k_nodes[seed.randrange(k_size)] w = l_nodes[seed.randrange(l_size)] # if nodes v and w are disconnected then attempt to connect if not G.has_edge(v, w) and (v != w): # if node v has no free stubs then do neighbor switch if h_node_residual[v] == 0: _neighbor_switch(G, v, k_unsat, h_node_residual) # if node w has no free stubs then do neighbor switch if h_node_residual[w] == 0: if k != l: _neighbor_switch(G, w, l_unsat, h_node_residual) else: _neighbor_switch(G, w, l_unsat, h_node_residual, avoid_node_id=v) # add edge (v, w) and update data structures G.add_edge(v, w) h_node_residual[v] -= 1 h_node_residual[w] -= 1 n_edges_add -= 1 if h_node_residual[v] == 0: k_unsat.discard(v) if h_node_residual[w] == 0: l_unsat.discard(w) return G