def test_valid_degree_sequence2():
n = 100
for i in range(10):
G = nx.barabasi_albert_graph(n,1)
deg = list(G.degree().values())
assert_true( nx.is_valid_degree_sequence(deg, method='eg') )
assert_true( nx.is_valid_degree_sequence(deg, method='hh') )
def test_valid_degree_sequence2():
n = 100
for i in range(10):
G = nx.barabasi_albert_graph(n, 1)
deg = (d for n, d in G.degree())
assert_true(nx.is_valid_degree_sequence(deg, method="eg"))
assert_true(nx.is_valid_degree_sequence(deg, method="hh"))
def test_valid_degree_sequence2():
n = 100
for i in range(10):
G = nx.barabasi_albert_graph(n,1)
deg = list(G.degree().values())
assert_true( nx.is_valid_degree_sequence(deg, method='eg') )
assert_true( nx.is_valid_degree_sequence(deg, method='hh') )
def test_valid_degree_sequence1():
n = 100
p = 0.3
for i in range(10):
G = nx.erdos_renyi_graph(n, p)
deg = (d for n, d in G.degree())
assert_true(nx.is_valid_degree_sequence(deg, method="eg"))
assert_true(nx.is_valid_degree_sequence(deg, method="hh"))
def test_valid_degree_sequence1():
n = 100
p = .3
for i in range(10):
G = nx.erdos_renyi_graph(n,p)
deg = list(G.degree().values())
assert_true( nx.is_valid_degree_sequence(deg, method='eg') )
assert_true( nx.is_valid_degree_sequence(deg, method='hh') )
def test_valid_degree_sequence1():
n = 100
p = .3
for i in range(10):
G = nx.erdos_renyi_graph(n,p)
deg = list(G.degree().values())
assert_true( nx.is_valid_degree_sequence(deg, method='eg') )
assert_true( nx.is_valid_degree_sequence(deg, method='hh') )
def gen_delta_graph(n, ks, ps, simple=False, model='conf'):
'''
Generate a graph whose degree distribution is of finite support.
Parameters
----------
n : number of nodes
ks : list
Degrees the nodes can have
ps : list
Probability for each edge, must have same length as ks and sum to 1
'''
# the following function generates a degree sequence of length n
pkmap = choicemap(ks,ps)
dseqfun = lambda n: [ nx.utils.weighted_choice(pkmap) for i in range(n) ]
success = False
while not success:
dseq = dseqfun(n)
if nx.is_valid_degree_sequence(dseq):
success = True
if model == 'conf':
G = nx.configuration_model(dseq)
elif model == 'chung-lu':
G = nx.expected_degree_graph(dseq)
else:
raise Exception('wrong graph model requested')
if simple:
G = nx.Graph(G) # removes multi-edges
G.remove_edges_from( G.selfloop_edges() ) # remove self loop
return G
def generate_graph(type = 'PL', n = 100, seed = 1.0, parameter = 2.1):
if type == 'ER':
G = nx.erdos_renyi_graph(n, p=parameter, seed=seed, directed=True)
G = nx.DiGraph(G)
G.remove_edges_from(G.selfloop_edges())
elif type == 'PL':
z = nx.utils.create_degree_sequence(n, nx.utils.powerlaw_sequence, exponent = parameter)
while not nx.is_valid_degree_sequence(z):
z = nx.utils.create_degree_sequence(n, nx.utils.powerlaw_sequence, exponent = parameter)
G = nx.configuration_model(z)
G = nx.DiGraph(G)
G.remove_edges_from(G.selfloop_edges())
elif type == 'BA':
G = nx.barabasi_albert_graph(n, 3, seed=None)
G = nx.DiGraph(G)
elif type == 'grid':
G = nx.grid_2d_graph(int(np.ceil(np.sqrt(n))), int(np.ceil(np.sqrt(n))))
G = nx.DiGraph(G)
elif type in ['facebook', 'enron', 'twitter', 'students', 'tumblr', 'facebookBig']:
#print 'start reading'
#_, G, _, _ = readRealGraph(os.path.join("..","..","Data", type+".txt"))
_, G, _, _ = readRealGraph(os.path.join("..","Data", type+".txt"))
print 'size of graph', G.number_of_nodes()
#Gcc = sorted(nx.connected_component_subgraphs(G.to_undirected()), key = len, reverse=True)
#print Gcc[0].number_of_nodes()
#print 'num of connected components', len(sorted(nx.connected_component_subgraphs(G.to_undirected()), key = len, reverse=True))
#exit()
if G.number_of_nodes() > n:
G = getSubgraph(G, n)
#G = getSubgraphSimulation(G, n, infP = 0.3)
#nx.draw(G)
#plt.show()
return G
def seqGen(n):
seq = [1, 2] #start with invalid deg seq
while not nx.is_valid_degree_sequence(seq):
seq = []
for x in range(0, n):
seq.append(random.randint(1, 4))
return seq
def generate_simple_graph(sfunction, N, avg_degree):
"""generate a simple random graph with sfunction degree sequence"""
graphical_deg_seq = False
is_connected_graph = False
while is_connected_graph == False:
while graphical_deg_seq == False:
seq = sfunction(N, avg_degree, seqtype="simple_degree")
graphical_deg_seq = nx.is_valid_degree_sequence(seq)
G = nx.havel_hakimi_graph(seq)
G.remove_edges_from(G.selfloop_edges())
if not nx.is_connected(G):
try:
connect_simple_graph(G)
is_connected_graph = True
randomize_graph(G)
except (IndexError):
is_connected_graph = False
if not nx.is_connected(G):
try:
connect_simple_graph(G)
is_connected_graph = True
except (IndexError):
is_connected_graph = False
graphical_deg_seq = False
return G
def generate_simple_graph(sfunction, N, avg_degree):
"""generate a simple random graph with sfunction degree sequence"""
graphical_deg_seq = False
is_connected_graph = False
while is_connected_graph == False:
while graphical_deg_seq == False:
seq = sfunction(N, avg_degree, seqtype="simple_degree")
graphical_deg_seq = nx.is_valid_degree_sequence(seq)
G = nx.havel_hakimi_graph(seq)
G.remove_edges_from(G.selfloop_edges())
if not nx.is_connected(G):
try:
connect_simple_graph(G)
is_connected_graph = True
randomize_graph(G)
except (IndexError):
is_connected_graph = False
if not nx.is_connected(G):
try:
connect_simple_graph(G)
is_connected_graph = True
except (IndexError):
is_connected_graph = False
graphical_deg_seq = False
return G
def create_total_degree_sequence (n, sfunction, avg_degree, mod_nodes, tolerance, max_tries=2000, **kwds):
"""
Creates a total-degree sequence.Ensures that the minimum degree is 1 and
the max degree is 1 less than the number of nodes and that the average
degree of the sequence generated is within a tolerance of 0.05.
`n`: number of nodes
`sfunction`: a sequence generating function with signature (number of
nodes, mean)
`avg_degree`: mean degree
`max_tries`: maximum number of tries before dying.
8Nov 2013: function now assigns total degree to the nodes module-wise, to
ensure that mean(d(k))= mean(d). ie., mean degree of each module is equal
to the network mean degree
"""
seqlist=[]
# this loop assumes modules are indexed sequentially from 0 to K-1
for mod in xrange(len(mod_nodes.keys())):
tries = 0
max_deg = len(mod_nodes[mod]) -1
is_valid_seq = False
tol = 5.0
while tol > tolerance or (not is_valid_seq) or (tries > max_tries):
trialseq = sfunction(len(mod_nodes[mod]), avg_degree, seqtype="degree")
seq = [min(max_deg, max( int(round(s)), 1 )) for s in trialseq]
is_valid_seq = nx.is_valid_degree_sequence(seq)
if not is_valid_seq and sum(seq)%2 !=0:
x = rnd.choice(xrange(len(seq)))
seq[x] += 1
is_valid_seq = nx.is_valid_degree_sequence(seq)
# check if d_k (bar) = d(bar)
tol = abs(avg_degree - np.mean(seq))
tries += 1
if (tries > max_tries):
raise nx.NetworkXError, \
"Exceeded max (%d) attempts at a valid sequence."%max_tries
seqlist.append(seq)
deg_list = [val for sublist in seqlist for val in sublist]
return deg_list
def __init__(self, degree, seed=None):
if not nx.is_valid_degree_sequence(degree):
raise nx.NetworkXUnfeasible('degree sequence is not graphical')
if seed is not None:
random.seed(seed)
self.degree = list(degree)
# node labels are integers 0,...,n-1
self.m = sum(self.degree)/2.0 # number of edges
try:
self.dmax = max(self.degree) # maximum degree
except ValueError:
self.dmax = 0
def __init__(self, degree, seed=None):
if not nx.is_valid_degree_sequence(degree):
raise nx.NetworkXUnfeasible('degree sequence is not graphical')
if seed is not None:
random.seed(seed)
self.degree = list(degree)
# node labels are integers 0,...,n-1
self.m = sum(self.degree)/2.0 # number of edges
try:
self.dmax = max(self.degree) # maximum degree
except ValueError:
self.dmax = 0
def test_small_graph_false():
z=[1000,3,3,3,3,2,2,2,1,1,1]
assert_false(nx.is_valid_degree_sequence(z, method='hh'))
assert_false(nx.is_valid_degree_sequence(z, method='eg'))
z=[6,5,4,4,2,1,1,1]
assert_false(nx.is_valid_degree_sequence(z, method='hh'))
assert_false(nx.is_valid_degree_sequence(z, method='eg'))
z=[1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
assert_false(nx.is_valid_degree_sequence(z, method='hh'))
assert_false(nx.is_valid_degree_sequence(z, method='eg'))
def test_small_graph_true():
z=[5,3,3,3,3,2,2,2,1,1,1]
assert_true(nx.is_valid_degree_sequence(z, method='hh'))
assert_true(nx.is_valid_degree_sequence(z, method='eg'))
z=[10,3,3,3,3,2,2,2,2,2,2]
assert_true(nx.is_valid_degree_sequence(z, method='hh'))
assert_true(nx.is_valid_degree_sequence(z, method='eg'))
z=[1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
assert_true(nx.is_valid_degree_sequence(z, method='hh'))
assert_true(nx.is_valid_degree_sequence(z, method='eg'))
def test_small_graph_false():
z=[1000,3,3,3,3,2,2,2,1,1,1]
assert_false(nx.is_valid_degree_sequence(z, method='hh'))
assert_false(nx.is_valid_degree_sequence(z, method='eg'))
z=[6,5,4,4,2,1,1,1]
assert_false(nx.is_valid_degree_sequence(z, method='hh'))
assert_false(nx.is_valid_degree_sequence(z, method='eg'))
z=[1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
assert_false(nx.is_valid_degree_sequence(z, method='hh'))
assert_false(nx.is_valid_degree_sequence(z, method='eg'))
def test_small_graph_true():
z=[5,3,3,3,3,2,2,2,1,1,1]
assert_true(nx.is_valid_degree_sequence(z, method='hh'))
assert_true(nx.is_valid_degree_sequence(z, method='eg'))
z=[10,3,3,3,3,2,2,2,2,2,2]
assert_true(nx.is_valid_degree_sequence(z, method='hh'))
assert_true(nx.is_valid_degree_sequence(z, method='eg'))
z=[1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
assert_true(nx.is_valid_degree_sequence(z, method='hh'))
assert_true(nx.is_valid_degree_sequence(z, method='eg'))
def undirected_degree_preserving_random_graph(degreeSequence):
"""Returns a random graph with n nodes, with the degree distribution same as that given by the argument ``degreeSequence``.
"""
assert (nx.is_valid_degree_sequence(degreeSequence))
num_nodes = len(degreeSequence)
G = nx.empty_graph(num_nodes, create_using)
num_degrees = []
for i in range(num_nodes):
num_degrees.append([])
dmax, dsum, n = 0, 0, 0
for d in degreeSequence:
if d > 0:
num_degrees[d].append(n)
dmax, dsum, n = max(dmax, d), dsum + d, n + 1
if n == 0:
return G
modstubs = [(0, 0)] * (dmax + 1)
while n > 0:
while len(num_degrees[dmax]) == 0:
dmax -= 1
if dmax > n - 1:
raise nx.NetworkXError(
'Sequence provided cannot be used on a graph.')
source = num_degrees[dmax].pop()
n -= 1
mslen = 0
k = dmax
for i in range(dmax):
while len(num_degrees[k]) == 0:
k -= 1
target = num_degrees[k].pop()
G.add_edge(source, target)
n -= 1
if k > 1:
modstubs[mslen] = (k - 1, target)
mslen += 1
for i in range(mslen):
(stubval, stubtarget) = modstubs[i]
num_degrees[stubval].append(stubtarget)
n += 1
return G
def scale_free_powerlaw(n=4000, lam=3, k_avg=4):
"""
n : number of nodes
lam : exponent
"""
noftries = 2147483647
standard = k_avg + 0.5
while (1):
s = generate(n, lam)
ratio = standard / np.mean(s)
for i in range(len(s)):
s[i] = int(ratio * s[i])
if (nx.is_valid_degree_sequence(s)):
G = nx.random_degree_sequence_graph(s, tries=noftries)
break
return G
def GenGraph(Nodes_CC,Lambda):
import numpy as np
import networkx as nx
Lmin=min(1,len(Nodes_CC))
Lmax=len(Nodes_CC)-1
flag=0
if len(Nodes_CC)==1:
D=[0]
while len(Nodes_CC)!=1 and flag==0:
D=[Distribution(Lmin,Lmax,Lambda) for i in range(0,len(Nodes_CC))]
if nx.is_valid_degree_sequence(D, method='eg') ==True:
# eg:Erdős-Gallai hh: Havel-Hakimi
flag=1
return D
def create_degree_sequence(n, sfunction=None, max_tries=50, **kwds):
_warnings.warn("create_degree_sequence() is deprecated",
DeprecationWarning)
""" Attempt to create a valid degree sequence of length n using
specified function sfunction(n,**kwds).
Parameters
----------
n : int
Length of degree sequence = number of nodes
sfunction: function
Function which returns a list of n real or integer values.
Called as "sfunction(n,**kwds)".
max_tries: int
Max number of attempts at creating valid degree sequence.
Notes
-----
Repeatedly create a degree sequence by calling sfunction(n,**kwds)
until achieving a valid degree sequence. If unsuccessful after
max_tries attempts, raise an exception.
For examples of sfunctions that return sequences of random numbers,
see networkx.Utils.
Examples
--------
>>> from networkx.utils import uniform_sequence, create_degree_sequence
>>> seq=create_degree_sequence(10,uniform_sequence)
"""
tries=0
max_deg=n
while tries < max_tries:
trialseq=sfunction(n,**kwds)
# round to integer values in the range [0,max_deg]
seq=[min(max_deg, max( int(round(s)),0 )) for s in trialseq]
# if graphical return, else throw away and try again
if nx.is_valid_degree_sequence(seq):
return seq
tries+=1
raise nx.NetworkXError(\
"Exceeded max (%d) attempts at a valid sequence."%max_tries)
def generate_graph(type='PL', n=100, seed=1.0, parameter=2.1):
if type == 'ER':
G = nx.erdos_renyi_graph(n, p=parameter, seed=seed, directed=True)
G = nx.DiGraph(G)
G.remove_edges_from(G.selfloop_edges())
elif type == 'PL':
z = nx.utils.create_degree_sequence(n,
nx.utils.powerlaw_sequence,
exponent=parameter)
while not nx.is_valid_degree_sequence(z):
z = nx.utils.create_degree_sequence(n,
nx.utils.powerlaw_sequence,
exponent=parameter)
G = nx.configuration_model(z)
G = nx.DiGraph(G)
G.remove_edges_from(G.selfloop_edges())
elif type == 'BA':
G = nx.barabasi_albert_graph(n, 3, seed=None)
G = nx.DiGraph(G)
elif type == 'grid':
G = nx.grid_2d_graph(int(np.ceil(np.sqrt(n))),
int(np.ceil(np.sqrt(n))))
G = nx.DiGraph(G)
elif type in [
'facebook', 'enron', 'twitter', 'students', 'tumblr', 'facebookBig'
]:
#print 'start reading'
#_, G, _, _ = readRealGraph(os.path.join("..","..","Data", type+".txt"))
_, G, _, _ = readRealGraph(os.path.join("..", "Data", type + ".txt"))
print 'size of graph', G.number_of_nodes()
#Gcc = sorted(nx.connected_component_subgraphs(G.to_undirected()), key = len, reverse=True)
#print Gcc[0].number_of_nodes()
#print 'num of connected components', len(sorted(nx.connected_component_subgraphs(G.to_undirected()), key = len, reverse=True))
#exit()
if G.number_of_nodes() > n:
G = getSubgraph(G, n)
#G = getSubgraphSimulation(G, n, infP = 0.3)
#nx.draw(G)
#plt.show()
return G
Random graph from given degree sequence. Draw degree histogram with matplotlib. """ __author__ = """Aric Hagberg ([email protected])""" try: import matplotlib.pyplot as plt import matplotlib except: raise import networkx as nx z=nx.utils.create_degree_sequence(100,nx.utils.powerlaw_sequence,exponent=2.1) nx.is_valid_degree_sequence(z) print "Configuration model" G=nx.configuration_model(z) # configuration model degree_sequence=sorted(nx.degree(G).values(),reverse=True) # degree sequence #print "Degree sequence", degree_sequence dmax=max(degree_sequence) plt.loglog(degree_sequence,'b-',marker='o') plt.title("Degree rank plot") plt.ylabel("degree") plt.xlabel("rank") # draw graph in inset plt.axes([0.45,0.45,0.45,0.45])
def test_iterable():
G = nx.path_graph(4)
seq = iter(G.degree().values())
assert_true(nx.is_valid_degree_sequence(seq, method='hh'))
assert_true(nx.is_valid_degree_sequence(seq, method='eg'))
def test_negative_input():
assert_false(nx.is_valid_degree_sequence([-1],'hh'))
assert_false(nx.is_valid_degree_sequence([-1],'eg'))
assert_false(nx.is_valid_degree_sequence([72.5],'eg'))
def test_string_input():
a = nx.is_valid_degree_sequence([],'foo')
def test_iterable():
G = nx.path_graph(4)
seq = iter(G.degree().values())
assert_true(nx.is_valid_degree_sequence(seq, method='hh'))
assert_true(nx.is_valid_degree_sequence(seq, method='eg'))
def havel_hakimi_graph(deg_sequence,create_using=None):
"""Return a simple graph with given degree sequence constructed
using the Havel-Hakimi algorithm.
Parameters
----------
deg_sequence: list of integers
Each integer corresponds to the degree of a node (need not be sorted).
create_using : graph, optional (default Graph)
Return graph of this type. The instance will be cleared.
Directed graphs are not allowed.
Raises
------
NetworkXException
For a non-graphical degree sequence (i.e. one
not realizable by some simple graph).
Notes
-----
The Havel-Hakimi algorithm constructs a simple graph by
successively connecting the node of highest degree to other nodes
of highest degree, resorting remaining nodes by degree, and
repeating the process. The resulting graph has a high
degree-associativity. Nodes are labeled 1,.., len(deg_sequence),
corresponding to their position in deg_sequence.
The basic algorithm is from Hakimi [1]_ and was generalized by
Kleitman and Wang [2]_.
References
----------
.. [1] Hakimi S., On Realizability of a Set of Integers as
Degrees of the Vertices of a Linear Graph. I,
Journal of SIAM, 10(3), pp. 496-506 (1962)
.. [2] Kleitman D.J. and Wang D.L.
Algorithms for Constructing Graphs and Digraphs with Given Valences
and Factors Discrete Mathematics, 6(1), pp. 79-88 (1973)
"""
if not nx.is_valid_degree_sequence(deg_sequence):
raise nx.NetworkXError('Invalid degree sequence')
if create_using is not None:
if create_using.is_directed():
raise nx.NetworkXError("Directed graphs are not supported")
p = len(deg_sequence)
G=nx.empty_graph(p,create_using)
num_degs = []
for i in range(p):
num_degs.append([])
dmax, dsum, n = 0, 0, 0
for d in deg_sequence:
# Process only the non-zero integers
if d>0:
num_degs[d].append(n)
dmax, dsum, n = max(dmax,d), dsum+d, n+1
# Return graph if no edges
if n==0:
return G
modstubs = [(0,0)]*(dmax+1)
# Successively reduce degree sequence by removing the maximum degree
while n > 0:
# Retrieve the maximum degree in the sequence
while len(num_degs[dmax]) == 0:
dmax -= 1
# If there are not enough stubs to connect to, then the sequence is
# not graphical
if dmax > n-1:
raise nx.NetworkXError('Non-graphical integer sequence')
# Remove largest stub in list
source = num_degs[dmax].pop()
n -= 1
# Reduce the next dmax largest stubs
mslen = 0
k = dmax
for i in range(dmax):
while len(num_degs[k]) == 0:
k -= 1
target = num_degs[k].pop()
G.add_edge(source, target)
n -= 1
if k > 1:
modstubs[mslen] = (k-1,target)
mslen += 1
# Add back to the list any nonzero stubs that were removed
for i in range(mslen):
(stubval, stubtarget) = modstubs[i]
num_degs[stubval].append(stubtarget)
n += 1
G.name="havel_hakimi_graph %d nodes %d edges"%(G.order(),G.size())
return G
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import random
DOPART1 = 1
DOPART2 = 1
#calculate mean fractional size per part 1, Q6
if (DOPART1):
NUMBERITERATIONS = 2663 #confidence level-99, confidence interval-2.5
resultsArray = np.zeros(NUMBERITERATIONS)
for index in range(0, NUMBERITERATIONS):
degreeSequence = [0] * 1000
degreeSequence[0] = 1
while (not nx.is_valid_degree_sequence(degreeSequence)):
for index2 in range(0, 1000): #generate degree sequence
rollTheDice = random.random()
if (rollTheDice <= .6):
degreeSequence[index2] = 1
else:
degreeSequence[index2] = 3
graph = nx.configuration_model(degreeSequence)
resultsArray[index] = len(
max(nx.connected_component_subgraphs(graph), key=len))
print "mean of largest component after", NUMBERITERATIONS, "iterations =", np.mean(
resultsArray)
print "mean fractional size =", np.mean(resultsArray) / 1000
if (DOPART2):
NUMBERITERATIONS = 666 #confidence level-99, confindence interval-5
dataset_tag=sys.argv[3]
num_iter=int(sys.argv[4]);
else:
print('Input is required as:\n 1) full edgelist filename
\n 2) name of output directory
\n 3) name tag for output files
\n 4) number of iterations');
sys.exit();
if not os.path.exists(dir):
os.makedirs(dir)
G=nx.read_weighted_edgelist(original_graph)
boo = nx.is_valid_degree_sequence(G.degree().values())
if boo == False:
print 'la successione dei gradi non e valida'
GD = nx.configuration_model(G.degree().values())
GD.remove_edges_from(GD.selfloop_edges())
GD = nx.Graph(GD)
ww = nx.utils.powerlaw_sequence(GD.number_of_edges())
m = min(ww)
M = max(ww)
def new_weights(ww):
return [(ww[i]-m)/(M-m) for i in range(len(ww))]
def test_string_input():
a = nx.is_valid_degree_sequence([],'foo')
def li_smax_graph(degree_seq, create_using=None):
"""Generates a graph based with a given degree sequence and maximizing
the s-metric. Experimental implementation.
Maximum s-metrix means that high degree nodes are connected to high
degree nodes.
- `degree_seq`: degree sequence, a list of integers with each entry
corresponding to the degree of a node.
A non-graphical degree sequence raises an Exception.
Reference::
@unpublished{li-2005,
author = {Lun Li and David Alderson and Reiko Tanaka
and John C. Doyle and Walter Willinger},
title = {Towards a Theory of Scale-Free Graphs:
Definition, Properties, and Implications (Extended Version)},
url = {http://arxiv.org/abs/cond-mat/0501169},
year = {2005}
}
The algorithm::
STEP 0 - Initialization
A = {0}
B = {1, 2, 3, ..., n}
O = {(i; j), ..., (k, l),...} where i < j, i <= k < l and
d_i * d_j >= d_k *d_l
wA = d_1
dB = sum(degrees)
STEP 1 - Link selection
(a) If |O| = 0 TERMINATE. Return graph A.
(b) Select element(s) (i, j) in O having the largest d_i * d_j , if for
any i or j either w_i = 0 or w_j = 0 delete (i, j) from O
(c) If there are no elements selected go to (a).
(d) Select the link (i, j) having the largest value w_i (where for each
(i, j) w_i is the smaller of w_i and w_j ), and proceed to STEP 2.
STEP 2 - Link addition
Type 1: i in A and j in B.
Add j to the graph A and remove it from the set B add a link
(i, j) to the graph A. Update variables:
wA = wA + d_j -2 and dB = dB - d_j
Decrement w_i and w_j with one. Delete (i, j) from O
Type 2: i and j in A.
Check Tree Condition: If dB = 2 * |B| - wA.
Delete (i, j) from O, continue to STEP 3
Check Disconnected Cluster Condition: If wA = 2.
Delete (i, j) from O, continue to STEP 3
Add the link (i, j) to the graph A
Decrement w_i and w_j with one, and wA = wA -2
STEP 3
Go to STEP 1
The article states that the algorithm will result in a maximal s-metric.
This implementation can not guarantee such maximality. I may have
misunderstood the algorithm, but I can not see how it can be anything
but a heuristic. Please contact me at [email protected] if you can
provide python code that can guarantee maximality.
Several optimizations are included in this code and it may be hard to read.
Commented code to come.
A POSSIBLE ALTERNATIVE:
For an 'unconstrained' graph, that is one they describe as having
the sum of the degree sequence be even(ie all undirected graphs) they
present a simpler algorithm. It is as follows
"For each vertex i: if di is even then attach di/2 self-loops;
if di is odd, then attach (di-1)/2 self-loops, leaving one
available "stub". Second for all remaining vertices with "stubs"
connect them in pairs according to decreasing values of di."[1]
Since this only works for undirected graphs anyway, perhaps this is
the better method? Note this also returns a graph with a larger
s_metric than the other method, and it seems to have the same
degree sequence, though I haven't tested it extensively.
"""
if not nx.is_valid_degree_sequence(degree_seq):
raise nx.NetworkXError('Invalid degree sequence')
if create_using is not None and create_using.is_directed():
raise nx.NetworkXError("Directed Graph not supported")
degree_seq.sort(reverse=True) # make sure it's sorted
if not (sum(degree_seq) %2): #check if the sum of the degree sequence is even, Should be if it is undirected
dseven = [di for di in degree_seq if (not di % 2)]
dsodd = [di for di in degree_seq if (di % 2)]
Gmax = nx.MultiGraph()
i = 0
for di in dseven:
Gmax.add_node(i)
for k in range(di // 2):
Gmax.add_edge(i,i)
i=i+1
for j in range(0,len(dsodd),2): #do this iteratively to connect
Gmax.add_node(i) #node stubs as we go
Gmax.add_node(i+1)
for k in range((dsodd[j]-1) // 2):
Gmax.add_edge(i,i)
for k in range((dsodd[j+1]-1) // 2):
Gmax.add_edge(i+1,i+1)
Gmax.add_edge(i,i+1)
i=i+2
return Gmax
else:
degrees_left = degree_seq[:]
A_graph = empty_graph(0,create_using)
A_graph.add_node(0)
a_list = [False]*len(degree_seq)
b_set = set(range(1,len(degree_seq)))
a_open = set([0])
O = []
for j in b_set:
heapq.heappush(O, (-degree_seq[0]*degree_seq[j], (0,j))),
wa = degrees_left[0] #stubs in a_graph
db = sum(degree_seq) - degree_seq[0] #stubs in b-graph
a_list[0] = True #node 0 is now in a_Graph
bsize = len(degree_seq) -1 #size of b_graph
selected = []
weight = 0
while O or selected:
if len(selected) <1 :
firstrun = True
while O:
(newweight, (i,j)) = heapq.heappop(O)
if degrees_left[i] < 1 or degrees_left[j] < 1 :
continue
if firstrun:
firstrun = False
weight = newweight
if not newweight == weight:
break
heapq.heappush(selected, [-degrees_left[i], \
-degrees_left[j], (i,j)])
if not weight == newweight:
heapq.heappush(O,(newweight, (i,j)))
weight *= -1
if len(selected) < 1:
break
[w1, w2, (i,j)] = heapq.heappop(selected)
if degrees_left[i] < 1 or degrees_left[j] < 1 :
continue
if a_list[i] and j in b_set:
#TYPE1
a_list[j] = True
b_set.remove(j)
A_graph.add_node(j)
A_graph.add_edge(i, j)
degrees_left[i] -= 1
degrees_left[j] -= 1
wa += degree_seq[j] - 2
db -= degree_seq[j]
bsize -= 1
newweight = weight
if not degrees_left[j] == 0:
a_open.add(j)
for k in b_set:
if A_graph.has_edge(j, k): continue
w = degree_seq[j]*degree_seq[k]
if w > newweight:
newweight = w
if weight == w and not newweight > weight:
heapq.heappush(selected, [-degrees_left[j], \
-degrees_left[k], (j,k)])
else:
heapq.heappush(O, (-w, (j,k)))
if not weight == newweight:
while selected:
[w1,w2,(i,j)] = heapq.heappop(selected)
if degrees_left[i]*degrees_left[j] > 0:
heapq.heappush(O, [-degree_seq[i]*degree_seq[j],(i,j)])
if degrees_left[i] == 0:
a_open.discard(i)
else:
#TYPE2
if db == (2*bsize - wa):
#tree condition
#print "removing because tree condition "
continue
elif db < 2*bsize -wa:
raise nx.NetworkXError(\
"THIS SHOULD NOT HAPPEN!-not graphable")
continue
elif wa == 2 and bsize > 0:
#print "removing because disconnected cluster"
#disconnected cluster condition
continue
elif wa == db - (bsize)*(bsize-1):
#print "MYOWN removing because disconnected cluster"
continue
A_graph.add_edge(i, j)
degrees_left[i] -= 1
degrees_left[j] -= 1
if degrees_left[i] < 1:
a_open.discard(i)
if degrees_left[j] < 1:
a_open.discard(j)
wa -= 2
if not degrees_left[i] < 0 and not degrees_left[j] < 0:
selected2 = (selected)
selected = []
while selected2:
[w1,w1, (i,j)] = heapq.heappop(selected2)
if degrees_left[i]*degrees_left[j] > 0:
heapq.heappush(selected, [-degrees_left[i], \
-degrees_left[j], (i,j)])
return A_graph
def create_indegree_sequence(n, m, sfunction, mod_nodes, wd, degree_list, tolerance, **kwds):
"""
Creates indegree sequence.
Ensures that (i)the within-module degree of node is less than or equal to
its total degree; (ii) the within-module degree sequence is graphical
nodes; and (iii) the average within module degree of the sequence
generated is within a tolerance of 0.05
'n': number of nodes
'sfunction': a sequence generating function with signature (number of
nodes, mean)
'wd' = mean within-module degree, m= total modules in the network
'nc'=average community size
'mod_nodes'= dictionary of nodal membership to communities
'avg_degree': mean degree
'degree_list'= total degree sequence
"""
is_valid_seq = False
is_valid_indegree = False
is_valid_module_size = False
is_valid_outdegree = True
tol = 5.0
connect_trial = 0
# Return empty list if the main loop breaks
indegree_list = []
while (not is_valid_seq) or (not is_valid_indegree) or (not is_valid_module_size) or (not is_valid_outdegree):
indegree_seq = sfunction(n, wd, seqtype="indegree")
indegree_sort = list(np.sort(indegree_seq))
degree_sort = list(np.sort(degree_list))
is_valid_indegree = all([indegree_sort[i] <= degree_sort[i] for i in range(n)]) == True
mod_sizes = [len(mod_nodes[x]) for x in mod_nodes.keys()]
is_valid_module_size = min(mod_sizes) >= max(indegree_seq)
is_valid_seq = True
if not is_valid_module_size:
connect_trial += 1
if is_valid_indegree and is_valid_seq and is_valid_module_size:
# assign within-degree to a node such that wd(i)<=d(i)
indegree_list = sort_inedge(indegree_sort, degree_sort, degree_list, n)
# if network has two modules then sum(outdegree) for module 1 has to be equal to sum(outdegree) of module 2
if m == 2:
mod1, mod2 = mod_nodes.keys()
is_valid_outdegree = sum([(degree_list[i] - indegree_list[i]) for i in mod_nodes[mod1]]) == sum(
[(degree_list[i] - indegree_list[i]) for i in mod_nodes[mod2]]
)
for module in mod_nodes:
seq = [indegree_list[i] for i in mod_nodes[module]]
while (sum(seq) % 2) != 0:
# choose a random node in the module
node_add_degree = rnd.choice([i for i in mod_nodes[module]])
# ensure that wd<=d and wd< (module size -1)after adding a within-degree to the node
if indegree_list[node_add_degree] < degree_list[node_add_degree] and indegree_list[
node_add_degree
] < len(mod_nodes[module]):
indegree_list[node_add_degree] += 1
seq = [indegree_list[i] for i in mod_nodes[module]]
is_valid_seq = nx.is_valid_degree_sequence(seq)
tol = abs(wd - (sum(seq) / (1.0 * len(seq)))) # ensure that wd_k(bar) = wd(bar)
if (not is_valid_seq) and tol > tolerance:
break
if connect_trial > 10:
break
return indegree_list
def havel_hakimi_graph(deg_sequence,create_using=None):
"""Return a simple graph with given degree sequence constructed
using the Havel-Hakimi algorithm.
Parameters
----------
deg_sequence: list of integers
Each integer corresponds to the degree of a node (need not be sorted).
create_using : graph, optional (default Graph)
Return graph of this type. The instance will be cleared.
Multigraphs and directed graphs are not allowed.
Raises
------
NetworkXException
For a non-graphical degree sequence (i.e. one
not realizable by some simple graph).
Notes
-----
The Havel-Hakimi algorithm constructs a simple graph by
successively connecting the node of highest degree to other nodes
of highest degree, resorting remaining nodes by degree, and
repeating the process. The resulting graph has a high
degree-associativity. Nodes are labeled 1,.., len(deg_sequence),
corresponding to their position in deg_sequence.
See Theorem 1.4 in [1]_.
This algorithm is also used in the function is_valid_degree_sequence.
References
----------
.. [1] G. Chartrand and L. Lesniak, "Graphs and Digraphs",
Chapman and Hall/CRC, 1996.
"""
if not nx.is_valid_degree_sequence(deg_sequence):
raise nx.NetworkXError('Invalid degree sequence')
if create_using is not None:
if create_using.is_directed():
raise nx.NetworkXError("Directed Graph not supported")
if create_using.is_multigraph():
raise nx.NetworkXError("Havel-Hakimi requires simple graph")
N=len(deg_sequence)
G=nx.empty_graph(N,create_using)
if N==0 or max(deg_sequence)==0: # done if no edges
return G
# form list of [stubs,name] for each node.
stublist=[ [deg_sequence[n],n] for n in G]
# Now connect the stubs
while stublist:
stublist.sort()
if stublist[0][0]<0: # took too many off some vertex
return False # should not happen if deg_seq is valid
(freestubs,source) = stublist.pop() # the node with the most stubs
if freestubs==0: break # the rest must also be 0 --Done!
if freestubs > len(stublist): # Trouble--can't make that many edges
return False # should not happen if deg_seq is valid
# attach edges to biggest nodes
for stubtarget in stublist[-freestubs:]:
G.add_edge(source, stubtarget[1])
stubtarget[0] -= 1 # updating stublist on the fly
G.name="havel_hakimi_graph %d nodes %d edges"%(G.order(),G.size())
return G
def havel_hakimi_graph(deg_sequence, create_using=None):
"""Return a simple graph with given degree sequence constructed
using the Havel-Hakimi algorithm.
Parameters
----------
deg_sequence: list of integers
Each integer corresponds to the degree of a node (need not be sorted).
create_using : graph, optional (default Graph)
Return graph of this type. The instance will be cleared.
Multigraphs and directed graphs are not allowed.
Raises
------
NetworkXException
For a non-graphical degree sequence (i.e. one
not realizable by some simple graph).
Notes
-----
The Havel-Hakimi algorithm constructs a simple graph by
successively connecting the node of highest degree to other nodes
of highest degree, resorting remaining nodes by degree, and
repeating the process. The resulting graph has a high
degree-associativity. Nodes are labeled 1,.., len(deg_sequence),
corresponding to their position in deg_sequence.
See Theorem 1.4 in [1]_.
This algorithm is also used in the function is_valid_degree_sequence.
References
----------
.. [1] G. Chartrand and L. Lesniak, "Graphs and Digraphs",
Chapman and Hall/CRC, 1996.
"""
if not nx.is_valid_degree_sequence(deg_sequence):
raise nx.NetworkXError('Invalid degree sequence')
if create_using is not None:
if create_using.is_directed():
raise nx.NetworkXError("Directed Graph not supported")
if create_using.is_multigraph():
raise nx.NetworkXError("Havel-Hakimi requires simple graph")
N = len(deg_sequence)
G = nx.empty_graph(N, create_using)
if N == 0 or max(deg_sequence) == 0: # done if no edges
return G
# form list of [stubs,name] for each node.
stublist = [[deg_sequence[n], n] for n in G]
# Now connect the stubs
while stublist:
stublist.sort()
if stublist[0][0] < 0: # took too many off some vertex
return False # should not happen if deg_seq is valid
(freestubs, source) = stublist.pop() # the node with the most stubs
if freestubs == 0: break # the rest must also be 0 --Done!
if freestubs > len(stublist): # Trouble--can't make that many edges
return False # should not happen if deg_seq is valid
# attach edges to biggest nodes
for stubtarget in stublist[-freestubs:]:
G.add_edge(source, stubtarget[1])
stubtarget[0] -= 1 # updating stublist on the fly
G.name = "havel_hakimi_graph %d nodes %d edges" % (G.order(), G.size())
return G
def test_negative_input():
assert_false(nx.is_valid_degree_sequence([-1],'hh'))
assert_false(nx.is_valid_degree_sequence([-1],'eg'))
assert_false(nx.is_valid_degree_sequence([72.5],'eg'))
def test_atlas():
for graph in nx.graph_atlas_g():
deg = list(graph.degree().values())
assert_true( nx.is_valid_degree_sequence(deg, method='eg') )
assert_true( nx.is_valid_degree_sequence(deg, method='hh') )
def test_negative_input():
assert_false(nx.is_valid_degree_sequence([-1], "hh"))
assert_false(nx.is_valid_degree_sequence([-1], "eg"))
assert_false(nx.is_valid_degree_sequence([72.5], "eg"))
def test_atlas(self):
for graph in self.GAG:
deg = (d for n, d in graph.degree())
assert_true(nx.is_valid_degree_sequence(deg, method="eg"))
assert_true(nx.is_valid_degree_sequence(deg, method="hh"))
dataset_tag=sys.argv[3]
num_iter=int(sys.argv[4]);
else:
print('Input is required as:\n 1) full edgelist filename
\n 2) name of output directory
\n 3) name tag for output files
\n 4) number of iterations');
sys.exit();
if not os.path.exists(dir):
os.makedirs(dir)
G=nx.read_weighted_edgelist(original_graph)
boo = nx.is_valid_degree_sequence(G.degree().values())
if boo == False:
print 'la successione dei gradi non e valida'
GD = nx.configuration_model(G.degree().values())
GD.remove_edges_from(GD.selfloop_edges())
GD = nx.Graph(GD)
ww = nx.utils.powerlaw_sequence(GD.number_of_edges(), 2.6)
m = min(ww)
M = max(ww)
def new_weights(ww):
return [(ww[i]-m)/(M-m) for i in range(len(ww))]
def test_atlas(self):
for graph in self.GAG:
deg = (d for n, d in graph.degree())
assert_true( nx.is_valid_degree_sequence(deg, method='eg') )
assert_true( nx.is_valid_degree_sequence(deg, method='hh') )
def setup_network(num_of_banks, num_of_assets, round_rvs, kind='man_half_half', p=2.5, min_holdings=10,
single_net=False, project_network=False, av_deg=False, add_fedfunds=None):
if not single_net:
if kind == 'er':
# np.random.seed(1)
if av_deg:
G = nx.bipartite.random_graph(num_of_banks, num_of_assets, av_deg/num_of_banks) # ,seed=1
else:
G = nx.bipartite.random_graph(num_of_banks, num_of_assets, np.mean(round_rvs)/num_of_banks) # ,seed=1
if not nx.is_connected(G):
G = max(nx.connected_component_subgraphs(G), key=len)
bot, top = nx.bipartite.sets(G)
assets = {t: 'a' + str(t-num_of_banks) for t in top}
banks = {b: 'b' + str(b) for b in bot}
z = assets.copy()
z.update(banks)
nx.relabel_nodes(G, z, copy=False)
for node, data in G.nodes(data=True):
if node.startswith('b'):
data['kind'] = 'bank'
elif node.startswith('a'):
data['kind'] = 'asset'
# print 'random: ', average_degree(G), len(G)
elif kind == 'powpow':
G = nx.bipartite.configuration_model(map(int, round_rvs), map(int, round_rvs), create_using=nx.Graph())
bot, top = nx.bipartite.sets(G)
assets = {t: 'a' + str(t-num_of_banks) for t in top}
banks = {b: 'b' + str(b) for b in bot}
z = assets.copy()
z.update(banks)
nx.relabel_nodes(G, z, copy=False)
for node,data in G.nodes(data=True):
if node.startswith('b'):
data['kind'] = 'bank'
else:
data['kind'] = 'asset'
# print 'powpow: ', average_degree(G)
elif kind == 'man_half_half':
banks = ['b'+str(i) for i in range(num_of_banks)]
assets = ['a'+str(i) for i in range(num_of_assets)]
G = nx.Graph()
G.add_nodes_from(banks,kind='bank')
G.add_nodes_from(assets,kind='asset')
# np.random.seed(1)
for i,j in enumerate(round_rvs):
G.add_edges_from([('b'+str(i),'a'+str(k)) for k in np.random.choice(num_of_assets,int(j),replace=False)])
# print 'power law: ', average_degree(G)
elif kind == 'auto_half_and_half':
temp_rvs = round_rvs[:-1]
# np.random.seed(1)
while True:
pois = np.random.poisson(np.mean(round_rvs),num_of_assets)
pd.Series(pois).hist()
rem = sum(pois)-sum(temp_rvs)
if rem < 0:
continue
else:
if ((p-1)*(min_holdings**(p-1)))*(rem**(-p)) > 0.0001:
round_rvs[-1] = rem
break
else:
continue
G = nx.bipartite.configuration_model(map(int, round_rvs), map(int, pois), create_using=nx.Graph())
bot,top = nx.bipartite.sets(G)
assets = {t: 'a' + str(t-num_of_banks) for t in top}
banks = {b: 'b' + str(b) for b in bot}
z = assets.copy()
z.update(banks)
nx.relabel_nodes(G,z,copy=False)
for node,data in G.nodes(data=True):
if node.startswith('b'):
data['kind'] = 'bank'
else:
data['kind'] = 'asset'
# print 'auto_h_h: ', average_degree(G)
if project_network:
G = nx.bipartite.project(G,[node for node,data in G.nodes(data=True) if data['kind']=='bank'])
for node,data in G.nodes(data=True):
data['init_deg'] = G.degree(node)
else:
if add_fedfunds:
G.add_node('f01')
new_edges = [('f01', 'b'+str(i)) for i in random.sample(range(num_of_banks), add_fedfunds)]
G.add_edges_from(new_edges)
G.node['f01']['kind'] = 'fed'
else:
if kind == 'er':
# np.random.seed(1)
G = nx.fast_gnp_random_graph(num_of_banks, np.mean(round_rvs)/num_of_banks)
elif kind == 'man_half_half':
if not nx.is_valid_degree_sequence(map(int,round_rvs)):
round_rvs[0] += 1
G = nx.configuration_model(map(int, round_rvs), create_using=nx.Graph())
banks = {b: 'b' + str(b) for b in range(num_of_banks)}
nx.relabel_nodes(G, banks, copy=False)
for node, data in G.nodes(data=True):
data['kind'] = 'bank'
for node,data in G.nodes(data=True):
data['init_deg'] = G.degree(node)
return G
def test_atlas():
for graph in nx.graph_atlas_g():
deg = list(graph.degree().values())
assert_true( nx.is_valid_degree_sequence(deg, method='eg') )
assert_true( nx.is_valid_degree_sequence(deg, method='hh') )
Random graph from given degree sequence. Draw degree histogram with matplotlib. """ __author__ = """Aric Hagberg ([email protected])""" try: import matplotlib.pyplot as plt import matplotlib except: raise import networkx as nx z=nx.create_degree_sequence(100,nx.utils.powerlaw_sequence,exponent=2.1) nx.is_valid_degree_sequence(z) print "Configuration model" G=nx.configuration_model(z) # configuration model degree_sequence=sorted(nx.degree(G).values(),reverse=True) # degree sequence #print "Degree sequence", degree_sequence dmax=max(degree_sequence) plt.loglog(degree_sequence,'b-',marker='o') plt.title("Degree rank plot") plt.ylabel("degree") plt.xlabel("rank") # draw graph in inset plt.axes([0.45,0.45,0.45,0.45])
def __init__(self, population,degree_seq=None,m=None):
# Create a population of agents
self.agents=list()
if type(population)==int and population>0:
for a in range(population):
self.agents.append(Agent(agent_id=a))
else:
raise ValueError("Model must have positive number of agents")
# Get total wealth in state
self.state_wealth=sum(map(lambda a: a.get_wealth(),self.agents))
if m is None:
self.threshold=.25*self.state_wealth
else:
if m>=0 and m<=1:
self.threshold=m*self.state_wealth
else:
raise ValueError("Value for m must be between 0 and 1")
# Create network
if degree_seq is None:
# If no degree sequence is provided create wealth-based preferential attachment
# This is the default setting for the model
for i in xrange(population):
for j in xrange(population):
if i!=j:
prob_tie=uniform(low=0,high=1)
# Tie probability function of agent's wealth relative to
# total wealth in state
if prob_tie<=self.agents[j].get_wealth()/self.state_wealth:
# Create symmetric ties between neighbors
self.agents[i].make_tie(self.agents[j])
self.agents[j].make_tie(self.agents[i])
else:
if(nx.is_valid_degree_sequence(degree_seq) and len(degree_seq)==population):
# Use NX configuration model to create network from degree sequence. By default,
# the configuration model returns a MultiGraph type with edges assigned at random.
# For consistency, the network type returned is Graph, and the random seed is
# always set to the number of agents in the environment.
G=nx.generators.configuration_model(degree_seq,create_using=nx.Graph(),seed=population)
for e in G.edges():
self.agents[e[0]].make_tie(self.agents[e[1]])
self.agents[e[1]].make_tie(self.agents[e[0]])
else:
raise nx.NetworkXError('Invalid degree sequence')
# Calculate all agent's m_net parameter
for a in self.agents:
agent_neighbors=a.get_neighbors()
# Get wealth of all neighbors
y_net=sum(map(lambda n: self.agents[n].get_wealth(),agent_neighbors))
# Calculate m_net
m_net=0
for n in agent_neighbors:
n_wealth=self.agents[n].get_wealth()
n_disposition=self.agents[n].get_disposition()
m_net+=n_disposition*(n_wealth/y_net)
a.set_mnet(m_net)
# Set all agents contribution levels based on their network position
for a in self.agents:
# Get all relevant agent info
agent_type=a.get_type()
agent_mnet=a.get_mnet()
agent_wealth=a.get_wealth()
agent_neighbors=a.get_neighbors()
if agent_type == 0:
# Altruistic type
a.set_contrib(0.5*a.get_disposition())
else:
if agent_type==1:
# Community type
unmet=self.threshold-agent_mnet # Level of weath needed to meet threshold
if unmet>0:
if unmet<agent_wealth:
a.set_contrib((unmet/agent_wealth)*a.get_disposition())
else:
# Agent commits all wealth if threshold out of reach
a.set_contrib(1.0*a.get_disposition())
else:
a.set_contrib(0.0)
else:
if agent_type==2:
# Min-match type
if agent_mnet>0:
min_neighbor=min(map(lambda n: self.agents[n].get_wealth(),agent_neighbors))
min_prop=min_neighbor/agent_mnet
if min_prop<1:
a.set_contrib(min_prop*a.get_disposition())
else:
a.set_contrib(1.0*a.get_disposition())
else:
a.set_contrib(random()*a.get_disposition())
else:
if agent_type==3:
# Max-match type
if agent_mnet>0:
max_neighbor=max(map(lambda n: self.agents[n].get_wealth(),agent_neighbors))
max_prop=max_neighbor/agent_mnet
if max_prop<1:
a.set_contrib(max_prop*a.get_disposition())
else:
a.set_contrib(1.0*a.get_disposition())
else:
a.set_contrib(random()*a.get_disposition())
else:
# Miserly type
a.set_contrib(uniform(0.0,0.05)*a.get_disposition())
# Finally, check to see if threshold has been met
self.total_contribs=sum(map(lambda a: a.get_contrib()*a.get_wealth(),self.agents))
if(self.total_contribs>=self.threshold):
self.threshold_met=True
else:
self.threshold_met=False