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Tiago Peixoto
graph-tool
Commits
30ac60fb
Commit
30ac60fb
authored
Jul 30, 2009
by
Tiago Peixoto
Browse files
Add random_graph() docstring
parent
3b958a22
Changes
1
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src/graph_tool/generation/__init__.py
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30ac60fb
...
...
@@ -35,6 +35,170 @@ def _corr_wrap(i, j, corr):
def
random_graph
(
N
,
deg_sampler
,
deg_corr
=
None
,
directed
=
True
,
parallel
=
False
,
self_loops
=
False
,
seed
=
0
,
verbose
=
False
):
r
"""
Generate a random graph, with a given degree distribution and correlation.
Parameters
----------
N : int
Number of vertices in the graph.
deg_sampler : function
A degree sampler function which is called without arguments, and returns
a tuple of ints representing the in and out-degree of a given vertex (or
a single int for undirected graphs, representing the out-degree). This
function is called once per vertex, but may be called more times, if the
degree sequence cannot be used to build a graph.
deg_corr : function (optional, default: None)
A function which give the degree correlation of the graph. It should be
callable with two parameters: the in,out-degree pair of the source
vertex an edge, and the in,out-degree pair of the target of the same
edge (for undirected graphs, both parameters are single values). The
function should return a number proportional to the probability of such
an edge existing in the generated graph.
directed : bool (optional, default: True)
Whether the generated graph should be directed.
parallel : bool (optional, default: False)
If True, parallel edges are allowed.
self_loops : bool (optional, default: False)
If True, self-loops are allowed.
seed : int (optional, default: 0)
Seed for the random number generator. If seed=0, a random value is
chosen.
Returns
-------
random_graph : Graph
The generated graph.
See Also
--------
random_rewire: in place graph shuffling
Notes
-----
The algorithm maintains a list of all available source and target degree
pairs, such that the deg_corr function is called only once with the same
parameters.
The uncorrelated case, the complexity is :math:`O(V+E)`. For the correlated
case the worst-case complexity is :math:`O(V^2)`, but the typical case has
complexity :math:`O(V+N_k^2)`, where :math:`N_k < V` is the number of
different degrees sampled (or in,out-degree pairs).
Examples
--------
>>> from numpy.random import randint, random, seed, poisson
>>> from pylab import *
>>> seed(42)
This is a degree sampler which uses rejection sampling to sample from the
distribution :math:`P(k)\propto 1/k`, up to a maximum.
>>> def sample_k(max):
... accept = False
... while not accept:
... k = randint(1,max+1)
... accept = random() < 1.0/k
... return k
...
The following generates a random undirected graph with degree distribution
:math:`P(k)\propto 1/k` (with k_max=40) and an *assortative* degree
correlation of the form:
.. math::
P(i,k) \propto \frac{1}{1+|i-k|}
>>> g = gt.random_graph(1000, lambda: sample_k(40),
... lambda i,k: 1.0/(1+abs(i-k)), directed=False)
>>> gt.scalar_assortativity(g, "out")
(0.52810631736984548, 0.012618649197538264)
The following samples an in,out-degree pair from the joint distribution:
.. math::
p(j,k) = \frac{1}{2}\frac{e^{-m_1}m_1^j}{j!}\frac{e^{-m_1}m_1^k}{k!} +
\frac{1}{2}\frac{e^{-m_2}m_2^j}{j!}\frac{e^{-m_2}m_2^k}{k!}
with :math:`m_1 = 4` and :math:`m_2 = 20`.
>>> def deg_sample():
... if random() > 0.5:
... return poisson(4), poisson(4)
... else:
... return poisson(20), poisson(20)
...
The following generates a random directed graph with this distribution, and
plots the combined degree correlation.
>>> g = gt.random_graph(20000, deg_sample)
>>>
>>> hist = gt.combined_corr_hist(g, "in", "out")
>>> imshow(hist[0], interpolation="nearest")
<...>
>>> colorbar()
<...>
>>> xlabel("in degree")
<...>
>>> ylabel("out degree")
<...>
>>> savefig("combined-deg-hist.png")
.. figure:: combined-deg-hist.png
:align: center
Combined degree histogram.
A correlated directed graph can be build as follows. Consider the following
degree correlation:
.. math::
P(j',k'|j,k)=\frac{e^{-k}k^{j'}}{j'!}
\frac{e^{-(20-j)}(20-j)^{k'}}{k'!}
i.e., the in->out correlation is "disassortative", the out->in correlation
is "assortative", and everything else is uncorrelated.
We will use a flat degree distribution in the range [1,20).
>>> p = scipy.stats.poisson
>>> g = gt.random_graph(20000, lambda: (sample_k(19), sample_k(19)),
... lambda a,b: (p.pmf(a[0],b[1])*
... p.pmf(a[1],20-b[0])))
Lets plot the average degree correlations to check.
>>> clf()
>>> corr = gt.avg_neighbour_corr(g, "in", "in")
>>> errorbar(corr[2], corr[0], yerr=corr[1], fmt="o-", label="<in> vs in")
(...)
>>> corr = gt.avg_neighbour_corr(g, "in", "out")
>>> errorbar(corr[2], corr[0], yerr=corr[1], fmt="o-", label="<out> vs in")
(...)
>>> corr = gt.avg_neighbour_corr(g, "out", "in")
>>> errorbar(corr[2], corr[0], yerr=corr[1], fmt="o-", label="<in> vs out")
(...)
>>> corr = gt.avg_neighbour_corr(g, "out", "out")
>>> errorbar(corr[2], corr[0], yerr=corr[1], fmt="o-", label="<out> vs out")
(...)
>>> legend(loc="best")
<...>
>>> xlabel("source degree")
<...>
>>> ylabel("average target degree")
<...>
>>> savefig("deg-corr-dir.png")
.. figure:: deg-corr-dir.png
:align: center
Average nearest neighbour correlations.
"""
if
seed
==
0
:
seed
=
numpy
.
random
.
randint
(
0
,
sys
.
maxint
)
g
=
Graph
()
...
...
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