# Source code for networkx.algorithms.threshold

```
"""
Threshold Graphs - Creation, manipulation and identification.
"""
from math import sqrt
import networkx as nx
from networkx.utils import py_random_state
__all__ = ["is_threshold_graph", "find_threshold_graph"]
[docs]
@nx._dispatchable
def is_threshold_graph(G):
"""
Returns `True` if `G` is a threshold graph.
Parameters
----------
G : NetworkX graph instance
An instance of `Graph`, `DiGraph`, `MultiGraph` or `MultiDiGraph`
Returns
-------
bool
`True` if `G` is a threshold graph, `False` otherwise.
Examples
--------
>>> from networkx.algorithms.threshold import is_threshold_graph
>>> G = nx.path_graph(3)
>>> is_threshold_graph(G)
True
>>> G = nx.barbell_graph(3, 3)
>>> is_threshold_graph(G)
False
References
----------
.. [1] Threshold graphs: https://en.wikipedia.org/wiki/Threshold_graph
"""
return is_threshold_sequence([d for n, d in G.degree()])
def is_threshold_sequence(degree_sequence):
"""
Returns True if the sequence is a threshold degree sequence.
Uses the property that a threshold graph must be constructed by
adding either dominating or isolated nodes. Thus, it can be
deconstructed iteratively by removing a node of degree zero or a
node that connects to the remaining nodes. If this deconstruction
fails then the sequence is not a threshold sequence.
"""
ds = degree_sequence[:] # get a copy so we don't destroy original
ds.sort()
while ds:
if ds[0] == 0: # if isolated node
ds.pop(0) # remove it
continue
if ds[-1] != len(ds) - 1: # is the largest degree node dominating?
return False # no, not a threshold degree sequence
ds.pop() # yes, largest is the dominating node
ds = [d - 1 for d in ds] # remove it and decrement all degrees
return True
def creation_sequence(degree_sequence, with_labels=False, compact=False):
"""
Determines the creation sequence for the given threshold degree sequence.
The creation sequence is a list of single characters 'd'
or 'i': 'd' for dominating or 'i' for isolated vertices.
Dominating vertices are connected to all vertices present when it
is added. The first node added is by convention 'd'.
This list can be converted to a string if desired using "".join(cs)
If with_labels==True:
Returns a list of 2-tuples containing the vertex number
and a character 'd' or 'i' which describes the type of vertex.
If compact==True:
Returns the creation sequence in a compact form that is the number
of 'i's and 'd's alternating.
Examples:
[1,2,2,3] represents d,i,i,d,d,i,i,i
[3,1,2] represents d,d,d,i,d,d
Notice that the first number is the first vertex to be used for
construction and so is always 'd'.
with_labels and compact cannot both be True.
Returns None if the sequence is not a threshold sequence
"""
if with_labels and compact:
raise ValueError("compact sequences cannot be labeled")
# make an indexed copy
if isinstance(degree_sequence, dict): # labeled degree sequence
ds = [[degree, label] for (label, degree) in degree_sequence.items()]
else:
ds = [[d, i] for i, d in enumerate(degree_sequence)]
ds.sort()
cs = [] # creation sequence
while ds:
if ds[0][0] == 0: # isolated node
(d, v) = ds.pop(0)
if len(ds) > 0: # make sure we start with a d
cs.insert(0, (v, "i"))
else:
cs.insert(0, (v, "d"))
continue
if ds[-1][0] != len(ds) - 1: # Not dominating node
return None # not a threshold degree sequence
(d, v) = ds.pop()
cs.insert(0, (v, "d"))
ds = [[d[0] - 1, d[1]] for d in ds] # decrement due to removing node
if with_labels:
return cs
if compact:
return make_compact(cs)
return [v[1] for v in cs] # not labeled
def make_compact(creation_sequence):
"""
Returns the creation sequence in a compact form
that is the number of 'i's and 'd's alternating.
Examples
--------
>>> from networkx.algorithms.threshold import make_compact
>>> make_compact(["d", "i", "i", "d", "d", "i", "i", "i"])
[1, 2, 2, 3]
>>> make_compact(["d", "d", "d", "i", "d", "d"])
[3, 1, 2]
Notice that the first number is the first vertex
to be used for construction and so is always 'd'.
Labeled creation sequences lose their labels in the
compact representation.
>>> make_compact([3, 1, 2])
[3, 1, 2]
"""
first = creation_sequence[0]
if isinstance(first, str): # creation sequence
cs = creation_sequence[:]
elif isinstance(first, tuple): # labeled creation sequence
cs = [s[1] for s in creation_sequence]
elif isinstance(first, int): # compact creation sequence
return creation_sequence
else:
raise TypeError("Not a valid creation sequence type")
ccs = []
count = 1 # count the run lengths of d's or i's.
for i in range(1, len(cs)):
if cs[i] == cs[i - 1]:
count += 1
else:
ccs.append(count)
count = 1
ccs.append(count) # don't forget the last one
return ccs
def uncompact(creation_sequence):
"""
Converts a compact creation sequence for a threshold
graph to a standard creation sequence (unlabeled).
If the creation_sequence is already standard, return it.
See creation_sequence.
"""
first = creation_sequence[0]
if isinstance(first, str): # creation sequence
return creation_sequence
elif isinstance(first, tuple): # labeled creation sequence
return creation_sequence
elif isinstance(first, int): # compact creation sequence
ccscopy = creation_sequence[:]
else:
raise TypeError("Not a valid creation sequence type")
cs = []
while ccscopy:
cs.extend(ccscopy.pop(0) * ["d"])
if ccscopy:
cs.extend(ccscopy.pop(0) * ["i"])
return cs
def creation_sequence_to_weights(creation_sequence):
"""
Returns a list of node weights which create the threshold
graph designated by the creation sequence. The weights
are scaled so that the threshold is 1.0. The order of the
nodes is the same as that in the creation sequence.
"""
# Turn input sequence into a labeled creation sequence
first = creation_sequence[0]
if isinstance(first, str): # creation sequence
if isinstance(creation_sequence, list):
wseq = creation_sequence[:]
else:
wseq = list(creation_sequence) # string like 'ddidid'
elif isinstance(first, tuple): # labeled creation sequence
wseq = [v[1] for v in creation_sequence]
elif isinstance(first, int): # compact creation sequence
wseq = uncompact(creation_sequence)
else:
raise TypeError("Not a valid creation sequence type")
# pass through twice--first backwards
wseq.reverse()
w = 0
prev = "i"
for j, s in enumerate(wseq):
if s == "i":
wseq[j] = w
prev = s
elif prev == "i":
prev = s
w += 1
wseq.reverse() # now pass through forwards
for j, s in enumerate(wseq):
if s == "d":
wseq[j] = w
prev = s
elif prev == "d":
prev = s
w += 1
# Now scale weights
if prev == "d":
w += 1
wscale = 1 / w
return [ww * wscale for ww in wseq]
# return wseq
def weights_to_creation_sequence(
weights, threshold=1, with_labels=False, compact=False
):
"""
Returns a creation sequence for a threshold graph
determined by the weights and threshold given as input.
If the sum of two node weights is greater than the
threshold value, an edge is created between these nodes.
The creation sequence is a list of single characters 'd'
or 'i': 'd' for dominating or 'i' for isolated vertices.
Dominating vertices are connected to all vertices present
when it is added. The first node added is by convention 'd'.
If with_labels==True:
Returns a list of 2-tuples containing the vertex number
and a character 'd' or 'i' which describes the type of vertex.
If compact==True:
Returns the creation sequence in a compact form that is the number
of 'i's and 'd's alternating.
Examples:
[1,2,2,3] represents d,i,i,d,d,i,i,i
[3,1,2] represents d,d,d,i,d,d
Notice that the first number is the first vertex to be used for
construction and so is always 'd'.
with_labels and compact cannot both be True.
"""
if with_labels and compact:
raise ValueError("compact sequences cannot be labeled")
# make an indexed copy
if isinstance(weights, dict): # labeled weights
wseq = [[w, label] for (label, w) in weights.items()]
else:
wseq = [[w, i] for i, w in enumerate(weights)]
wseq.sort()
cs = [] # creation sequence
cutoff = threshold - wseq[-1][0]
while wseq:
if wseq[0][0] < cutoff: # isolated node
(w, label) = wseq.pop(0)
cs.append((label, "i"))
else:
(w, label) = wseq.pop()
cs.append((label, "d"))
cutoff = threshold - wseq[-1][0]
if len(wseq) == 1: # make sure we start with a d
(w, label) = wseq.pop()
cs.append((label, "d"))
# put in correct order
cs.reverse()
if with_labels:
return cs
if compact:
return make_compact(cs)
return [v[1] for v in cs] # not labeled
# Manipulating NetworkX.Graphs in context of threshold graphs
@nx._dispatchable(graphs=None, returns_graph=True)
def threshold_graph(creation_sequence, create_using=None):
"""
Create a threshold graph from the creation sequence or compact
creation_sequence.
The input sequence can be a
creation sequence (e.g. ['d','i','d','d','d','i'])
labeled creation sequence (e.g. [(0,'d'),(2,'d'),(1,'i')])
compact creation sequence (e.g. [2,1,1,2,0])
Use cs=creation_sequence(degree_sequence,labeled=True)
to convert a degree sequence to a creation sequence.
Returns None if the sequence is not valid
"""
# Turn input sequence into a labeled creation sequence
first = creation_sequence[0]
if isinstance(first, str): # creation sequence
ci = list(enumerate(creation_sequence))
elif isinstance(first, tuple): # labeled creation sequence
ci = creation_sequence[:]
elif isinstance(first, int): # compact creation sequence
cs = uncompact(creation_sequence)
ci = list(enumerate(cs))
else:
print("not a valid creation sequence type")
return None
G = nx.empty_graph(0, create_using)
if G.is_directed():
raise nx.NetworkXError("Directed Graph not supported")
G.name = "Threshold Graph"
# add nodes and edges
# if type is 'i' just add nodea
# if type is a d connect to everything previous
while ci:
(v, node_type) = ci.pop(0)
if node_type == "d": # dominating type, connect to all existing nodes
# We use `for u in list(G):` instead of
# `for u in G:` because we edit the graph `G` in
# the loop. Hence using an iterator will result in
# `RuntimeError: dictionary changed size during iteration`
for u in list(G):
G.add_edge(v, u)
G.add_node(v)
return G
@nx._dispatchable
def find_alternating_4_cycle(G):
"""
Returns False if there aren't any alternating 4 cycles.
Otherwise returns the cycle as [a,b,c,d] where (a,b)
and (c,d) are edges and (a,c) and (b,d) are not.
"""
for u, v in G.edges():
for w in G.nodes():
if not G.has_edge(u, w) and u != w:
for x in G.neighbors(w):
if not G.has_edge(v, x) and v != x:
return [u, v, w, x]
return False
[docs]
@nx._dispatchable(returns_graph=True)
def find_threshold_graph(G, create_using=None):
"""
Returns a threshold subgraph that is close to largest in `G`.
The threshold graph will contain the largest degree node in G.
Parameters
----------
G : NetworkX graph instance
An instance of `Graph`, or `MultiDiGraph`
create_using : NetworkX graph class or `None` (default), optional
Type of graph to use when constructing the threshold graph.
If `None`, infer the appropriate graph type from the input.
Returns
-------
graph :
A graph instance representing the threshold graph
Examples
--------
>>> from networkx.algorithms.threshold import find_threshold_graph
>>> G = nx.barbell_graph(3, 3)
>>> T = find_threshold_graph(G)
>>> T.nodes # may vary
NodeView((7, 8, 5, 6))
References
----------
.. [1] Threshold graphs: https://en.wikipedia.org/wiki/Threshold_graph
"""
return threshold_graph(find_creation_sequence(G), create_using)
@nx._dispatchable
def find_creation_sequence(G):
"""
Find a threshold subgraph that is close to largest in G.
Returns the labeled creation sequence of that threshold graph.
"""
cs = []
# get a local pointer to the working part of the graph
H = G
while H.order() > 0:
# get new degree sequence on subgraph
dsdict = dict(H.degree())
ds = [(d, v) for v, d in dsdict.items()]
ds.sort()
# Update threshold graph nodes
if ds[-1][0] == 0: # all are isolated
cs.extend(zip(dsdict, ["i"] * (len(ds) - 1) + ["d"]))
break # Done!
# pull off isolated nodes
while ds[0][0] == 0:
(d, iso) = ds.pop(0)
cs.append((iso, "i"))
# find new biggest node
(d, bigv) = ds.pop()
# add edges of star to t_g
cs.append((bigv, "d"))
# form subgraph of neighbors of big node
H = H.subgraph(H.neighbors(bigv))
cs.reverse()
return cs
# Properties of Threshold Graphs
def triangles(creation_sequence):
"""
Compute number of triangles in the threshold graph with the
given creation sequence.
"""
# shortcut algorithm that doesn't require computing number
# of triangles at each node.
cs = creation_sequence # alias
dr = cs.count("d") # number of d's in sequence
ntri = dr * (dr - 1) * (dr - 2) / 6 # number of triangles in clique of nd d's
# now add dr choose 2 triangles for every 'i' in sequence where
# dr is the number of d's to the right of the current i
for i, typ in enumerate(cs):
if typ == "i":
ntri += dr * (dr - 1) / 2
else:
dr -= 1
return ntri
def triangle_sequence(creation_sequence):
"""
Return triangle sequence for the given threshold graph creation sequence.
"""
cs = creation_sequence
seq = []
dr = cs.count("d") # number of d's to the right of the current pos
dcur = (dr - 1) * (dr - 2) // 2 # number of triangles through a node of clique dr
irun = 0 # number of i's in the last run
drun = 0 # number of d's in the last run
for i, sym in enumerate(cs):
if sym == "d":
drun += 1
tri = dcur + (dr - 1) * irun # new triangles at this d
else: # cs[i]="i":
if prevsym == "d": # new string of i's
dcur += (dr - 1) * irun # accumulate shared shortest paths
irun = 0 # reset i run counter
dr -= drun # reduce number of d's to right
drun = 0 # reset d run counter
irun += 1
tri = dr * (dr - 1) // 2 # new triangles at this i
seq.append(tri)
prevsym = sym
return seq
def cluster_sequence(creation_sequence):
"""
Return cluster sequence for the given threshold graph creation sequence.
"""
triseq = triangle_sequence(creation_sequence)
degseq = degree_sequence(creation_sequence)
cseq = []
for i, deg in enumerate(degseq):
tri = triseq[i]
if deg <= 1: # isolated vertex or single pair gets cc 0
cseq.append(0)
continue
max_size = (deg * (deg - 1)) // 2
cseq.append(tri / max_size)
return cseq
def degree_sequence(creation_sequence):
"""
Return degree sequence for the threshold graph with the given
creation sequence
"""
cs = creation_sequence # alias
seq = []
rd = cs.count("d") # number of d to the right
for i, sym in enumerate(cs):
if sym == "d":
rd -= 1
seq.append(rd + i)
else:
seq.append(rd)
return seq
def density(creation_sequence):
"""
Return the density of the graph with this creation_sequence.
The density is the fraction of possible edges present.
"""
N = len(creation_sequence)
two_size = sum(degree_sequence(creation_sequence))
two_possible = N * (N - 1)
den = two_size / two_possible
return den
def degree_correlation(creation_sequence):
"""
Return the degree-degree correlation over all edges.
"""
cs = creation_sequence
s1 = 0 # deg_i*deg_j
s2 = 0 # deg_i^2+deg_j^2
s3 = 0 # deg_i+deg_j
m = 0 # number of edges
rd = cs.count("d") # number of d nodes to the right
rdi = [i for i, sym in enumerate(cs) if sym == "d"] # index of "d"s
ds = degree_sequence(cs)
for i, sym in enumerate(cs):
if sym == "d":
if i != rdi[0]:
print("Logic error in degree_correlation", i, rdi)
raise ValueError
rdi.pop(0)
degi = ds[i]
for dj in rdi:
degj = ds[dj]
s1 += degj * degi
s2 += degi**2 + degj**2
s3 += degi + degj
m += 1
denom = 2 * m * s2 - s3 * s3
numer = 4 * m * s1 - s3 * s3
if denom == 0:
if numer == 0:
return 1
raise ValueError(f"Zero Denominator but Numerator is {numer}")
return numer / denom
def shortest_path(creation_sequence, u, v):
"""
Find the shortest path between u and v in a
threshold graph G with the given creation_sequence.
For an unlabeled creation_sequence, the vertices
u and v must be integers in (0,len(sequence)) referring
to the position of the desired vertices in the sequence.
For a labeled creation_sequence, u and v are labels of vertices.
Use cs=creation_sequence(degree_sequence,with_labels=True)
to convert a degree sequence to a creation sequence.
Returns a list of vertices from u to v.
Example: if they are neighbors, it returns [u,v]
"""
# Turn input sequence into a labeled creation sequence
first = creation_sequence[0]
if isinstance(first, str): # creation sequence
cs = [(i, creation_sequence[i]) for i in range(len(creation_sequence))]
elif isinstance(first, tuple): # labeled creation sequence
cs = creation_sequence[:]
elif isinstance(first, int): # compact creation sequence
ci = uncompact(creation_sequence)
cs = [(i, ci[i]) for i in range(len(ci))]
else:
raise TypeError("Not a valid creation sequence type")
verts = [s[0] for s in cs]
if v not in verts:
raise ValueError(f"Vertex {v} not in graph from creation_sequence")
if u not in verts:
raise ValueError(f"Vertex {u} not in graph from creation_sequence")
# Done checking
if u == v:
return [u]
uindex = verts.index(u)
vindex = verts.index(v)
bigind = max(uindex, vindex)
if cs[bigind][1] == "d":
return [u, v]
# must be that cs[bigind][1]=='i'
cs = cs[bigind:]
while cs:
vert = cs.pop()
if vert[1] == "d":
return [u, vert[0], v]
# All after u are type 'i' so no connection
return -1
def shortest_path_length(creation_sequence, i):
"""
Return the shortest path length from indicated node to
every other node for the threshold graph with the given
creation sequence.
Node is indicated by index i in creation_sequence unless
creation_sequence is labeled in which case, i is taken to
be the label of the node.
Paths lengths in threshold graphs are at most 2.
Length to unreachable nodes is set to -1.
"""
# Turn input sequence into a labeled creation sequence
first = creation_sequence[0]
if isinstance(first, str): # creation sequence
if isinstance(creation_sequence, list):
cs = creation_sequence[:]
else:
cs = list(creation_sequence)
elif isinstance(first, tuple): # labeled creation sequence
cs = [v[1] for v in creation_sequence]
i = [v[0] for v in creation_sequence].index(i)
elif isinstance(first, int): # compact creation sequence
cs = uncompact(creation_sequence)
else:
raise TypeError("Not a valid creation sequence type")
# Compute
N = len(cs)
spl = [2] * N # length 2 to every node
spl[i] = 0 # except self which is 0
# 1 for all d's to the right
for j in range(i + 1, N):
if cs[j] == "d":
spl[j] = 1
if cs[i] == "d": # 1 for all nodes to the left
for j in range(i):
spl[j] = 1
# and -1 for any trailing i to indicate unreachable
for j in range(N - 1, 0, -1):
if cs[j] == "d":
break
spl[j] = -1
return spl
def betweenness_sequence(creation_sequence, normalized=True):
"""
Return betweenness for the threshold graph with the given creation
sequence. The result is unscaled. To scale the values
to the interval [0,1] divide by (n-1)*(n-2).
"""
cs = creation_sequence
seq = [] # betweenness
lastchar = "d" # first node is always a 'd'
dr = float(cs.count("d")) # number of d's to the right of current pos
irun = 0 # number of i's in the last run
drun = 0 # number of d's in the last run
dlast = 0.0 # betweenness of last d
for i, c in enumerate(cs):
if c == "d": # cs[i]=="d":
# betweenness = amt shared with earlier d's and i's
# + new isolated nodes covered
# + new paths to all previous nodes
b = dlast + (irun - 1) * irun / dr + 2 * irun * (i - drun - irun) / dr
drun += 1 # update counter
else: # cs[i]="i":
if lastchar == "d": # if this is a new run of i's
dlast = b # accumulate betweenness
dr -= drun # update number of d's to the right
drun = 0 # reset d counter
irun = 0 # reset i counter
b = 0 # isolated nodes have zero betweenness
irun += 1 # add another i to the run
seq.append(float(b))
lastchar = c
# normalize by the number of possible shortest paths
if normalized:
order = len(cs)
scale = 1.0 / ((order - 1) * (order - 2))
seq = [s * scale for s in seq]
return seq
def eigenvectors(creation_sequence):
"""
Return a 2-tuple of Laplacian eigenvalues and eigenvectors
for the threshold network with creation_sequence.
The first value is a list of eigenvalues.
The second value is a list of eigenvectors.
The lists are in the same order so corresponding eigenvectors
and eigenvalues are in the same position in the two lists.
Notice that the order of the eigenvalues returned by eigenvalues(cs)
may not correspond to the order of these eigenvectors.
"""
ccs = make_compact(creation_sequence)
N = sum(ccs)
vec = [0] * N
val = vec[:]
# get number of type d nodes to the right (all for first node)
dr = sum(ccs[::2])
nn = ccs[0]
vec[0] = [1.0 / sqrt(N)] * N
val[0] = 0
e = dr
dr -= nn
type_d = True
i = 1
dd = 1
while dd < nn:
scale = 1.0 / sqrt(dd * dd + i)
vec[i] = i * [-scale] + [dd * scale] + [0] * (N - i - 1)
val[i] = e
i += 1
dd += 1
if len(ccs) == 1:
return (val, vec)
for nn in ccs[1:]:
scale = 1.0 / sqrt(nn * i * (i + nn))
vec[i] = i * [-nn * scale] + nn * [i * scale] + [0] * (N - i - nn)
# find eigenvalue
type_d = not type_d
if type_d:
e = i + dr
dr -= nn
else:
e = dr
val[i] = e
st = i
i += 1
dd = 1
while dd < nn:
scale = 1.0 / sqrt(i - st + dd * dd)
vec[i] = [0] * st + (i - st) * [-scale] + [dd * scale] + [0] * (N - i - 1)
val[i] = e
i += 1
dd += 1
return (val, vec)
def spectral_projection(u, eigenpairs):
"""
Returns the coefficients of each eigenvector
in a projection of the vector u onto the normalized
eigenvectors which are contained in eigenpairs.
eigenpairs should be a list of two objects. The
first is a list of eigenvalues and the second a list
of eigenvectors. The eigenvectors should be lists.
There's not a lot of error checking on lengths of
arrays, etc. so be careful.
"""
coeff = []
evect = eigenpairs[1]
for ev in evect:
c = sum(evv * uv for (evv, uv) in zip(ev, u))
coeff.append(c)
return coeff
def eigenvalues(creation_sequence):
"""
Return sequence of eigenvalues of the Laplacian of the threshold
graph for the given creation_sequence.
Based on the Ferrer's diagram method. The spectrum is integral
and is the conjugate of the degree sequence.
See::
@Article{degree-merris-1994,
author = {Russel Merris},
title = {Degree maximal graphs are Laplacian integral},
journal = {Linear Algebra Appl.},
year = {1994},
volume = {199},
pages = {381--389},
}
"""
degseq = degree_sequence(creation_sequence)
degseq.sort()
eiglist = [] # zero is always one eigenvalue
eig = 0
row = len(degseq)
bigdeg = degseq.pop()
while row:
if bigdeg < row:
eiglist.append(eig)
row -= 1
else:
eig += 1
if degseq:
bigdeg = degseq.pop()
else:
bigdeg = 0
return eiglist
# Threshold graph creation routines
@py_random_state(2)
def random_threshold_sequence(n, p, seed=None):
"""
Create a random threshold sequence of size n.
A creation sequence is built by randomly choosing d's with
probability p and i's with probability 1-p.
s=nx.random_threshold_sequence(10,0.5)
returns a threshold sequence of length 10 with equal
probably of an i or a d at each position.
A "random" threshold graph can be built with
G=nx.threshold_graph(s)
seed : integer, random_state, or None (default)
Indicator of random number generation state.
See :ref:`Randomness<randomness>`.
"""
if not (0 <= p <= 1):
raise ValueError("p must be in [0,1]")
cs = ["d"] # threshold sequences always start with a d
for i in range(1, n):
if seed.random() < p:
cs.append("d")
else:
cs.append("i")
return cs
# maybe *_d_threshold_sequence routines should
# be (or be called from) a single routine with a more descriptive name
# and a keyword parameter?
def right_d_threshold_sequence(n, m):
"""
Create a skewed threshold graph with a given number
of vertices (n) and a given number of edges (m).
The routine returns an unlabeled creation sequence
for the threshold graph.
FIXME: describe algorithm
"""
cs = ["d"] + ["i"] * (n - 1) # create sequence with n insolated nodes
# m <n : not enough edges, make disconnected
if m < n:
cs[m] = "d"
return cs
# too many edges
if m > n * (n - 1) / 2:
raise ValueError("Too many edges for this many nodes.")
# connected case m >n-1
ind = n - 1
sum = n - 1
while sum < m:
cs[ind] = "d"
ind -= 1
sum += ind
ind = m - (sum - ind)
cs[ind] = "d"
return cs
def left_d_threshold_sequence(n, m):
"""
Create a skewed threshold graph with a given number
of vertices (n) and a given number of edges (m).
The routine returns an unlabeled creation sequence
for the threshold graph.
FIXME: describe algorithm
"""
cs = ["d"] + ["i"] * (n - 1) # create sequence with n insolated nodes
# m <n : not enough edges, make disconnected
if m < n:
cs[m] = "d"
return cs
# too many edges
if m > n * (n - 1) / 2:
raise ValueError("Too many edges for this many nodes.")
# Connected case when M>N-1
cs[n - 1] = "d"
sum = n - 1
ind = 1
while sum < m:
cs[ind] = "d"
sum += ind
ind += 1
if sum > m: # be sure not to change the first vertex
cs[sum - m] = "i"
return cs
@py_random_state(3)
def swap_d(cs, p_split=1.0, p_combine=1.0, seed=None):
"""
Perform a "swap" operation on a threshold sequence.
The swap preserves the number of nodes and edges
in the graph for the given sequence.
The resulting sequence is still a threshold sequence.
Perform one split and one combine operation on the
'd's of a creation sequence for a threshold graph.
This operation maintains the number of nodes and edges
in the graph, but shifts the edges from node to node
maintaining the threshold quality of the graph.
seed : integer, random_state, or None (default)
Indicator of random number generation state.
See :ref:`Randomness<randomness>`.
"""
# preprocess the creation sequence
dlist = [i for (i, node_type) in enumerate(cs[1:-1]) if node_type == "d"]
# split
if seed.random() < p_split:
choice = seed.choice(dlist)
split_to = seed.choice(range(choice))
flip_side = choice - split_to
if split_to != flip_side and cs[split_to] == "i" and cs[flip_side] == "i":
cs[choice] = "i"
cs[split_to] = "d"
cs[flip_side] = "d"
dlist.remove(choice)
# don't add or combine may reverse this action
# dlist.extend([split_to,flip_side])
# print >>sys.stderr,"split at %s to %s and %s"%(choice,split_to,flip_side)
# combine
if seed.random() < p_combine and dlist:
first_choice = seed.choice(dlist)
second_choice = seed.choice(dlist)
target = first_choice + second_choice
if target >= len(cs) or cs[target] == "d" or first_choice == second_choice:
return cs
# OK to combine
cs[first_choice] = "i"
cs[second_choice] = "i"
cs[target] = "d"
# print >>sys.stderr,"combine %s and %s to make %s."%(first_choice,second_choice,target)
return cs
```