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Generating random regular graphs. J.H. Kim (Microsoft) V. Vu (UCSD).

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generating random regular graphs

Generating random regular graphs

J.H. Kim (Microsoft)

V. Vu (UCSD)

slide3
Configuration method (Bender-Canfield, Bollobas 80’s): Consider n sets of size d. Draw a random prefect matching on nd points. Shrink each set into a vertex.

Trouble: With very high probability, the resulting graph is not simple. In fact, the probability that it is simple is only exp (- d2 / 4).

(1) Practical: Reasonable only if d very small (less than 10, say).

(2) Theoretical: Can be used to prove theorems for small d, typically d= O(ln n).

slide5
Markov chain method (Jerrum-Sinclair 90) Create a master graph whose perfect matchings correspond to simple regular graphs. Use a Markov chain to produce a random perfect matching.
  • Practical: Work for all d in polynomial time. However the running time is n10.
  • Theoretical:

McKay-Wormald’s algorithm (90): nd3 time, but hard to implement.

slide6
Steger-Wormald’s algorithm (97): Use the idea of the configuration method, but now we build the perfect matching gradually, one edge per step, avoiding loops and parallel edges.

Start with n sets of size d. Assume that m pairs have been chosen. Delete all pairs that are parallel with these edges and also all pairs that form loops. Among the available pairs, choose one uniformly randomly.

Start with n points. Assume that m edges have been drawn. Draw a new edge uv with probability proportional to (d-du)(d-dv).

slide8
Trivially the output is a simple d-regular graph, but it is unclear what the distribution is.

This algorithm is fast (nd2) and very easy to implement. Recently Kim-Vu, using a coupling argument, show that the graph created by this algorithm behaves essentially like an Erdos-Renyi’s random graph with the same density. So if we can prove that the output is indeed G(n,d), we can derive lots of results for random regular graphs.

slide9
Steger and Wormald (1996) proved that the distribution in question is asymptotically uniform, for d= o(n1/28). They conjecture that it remains uniform as far as

d =o( n1/3).

Main theorem (Kim-V. 02) The distribution is asymptotic uniform for d = o(n1/3).

Corollary: all properties of Erdos-Renyi graphs (hamiltonianity, colorability etc) hold for random regular graphs G(n,d) in this range.

slide10
A reverse philosophy:

In a typical situation, one uses Markov chains to generate a uniform random sample and use this to compute the number of objects.

In the current problem, we know the number of random regular graphs (via combinatorial arguments) and use this information to prove that the graph generated by the algorithm is indeed uniformly random.

slide11
McKay and Wormald shown that for d= o(n1/3) the number of d-regular graphs is:

It is well-known (and easy to check) that for each d-regular graph G, there are exactly (d!)n different simple matchings which give rise to G. So to prove our theorem, it suffices to show that if d=o(n1/3), then for any simple perfect matching M

slide12
Consider an ordering M =x1, …, xnd/2 where xm are the edges of M. Assume that the first m edges of M are obtained and let Gm(M) be the graph formed by the projection of these edges. Thus Gm(M) is a subgraph of G. To count the number of suitable edges, notice that there are ways to form an edge.

However, an edge is suitable if and only if it does not join two vertices from the same group or two vertices come from two already adjacent groups. The number of edges of the first type is

and the number of unsuitable edges of the second type is

where du is the degree of u in Gm (M).

slide13
Set ,the number of suitable edges is

It follows that

Now it suffices to prove that

slide14
Let δm be the expectation of ∆m It is easy to find a formula for δm. Moreover, replacing ∆m by δm we have

so all we need to do is to justify this replacing, namely to show that

slide15
The main point here now is to show that for a typical m, the ratio

is very very close to 1.

For a moment let us assume that the main part of ∆m (M) is

We want to show that this random variable is very close to its expectation.

slide16
du

dv

u

e

v

f

g

We can think of the projected graph Gm (M) as a random subgraph of G where each edge is chosen with probability p= m/(nd/2); te is the indicator of the event that the edge e is chosen. So can be written as

slide17
We want to show that

is very strongly concentrated

(1) Azuma’s inequality (Steger-Wormald):d =o(n 1/28 ).

Azuma’s inequality tells that a random variable with small Lipschitz coefficient is strongly concentrated around its mean.

The trouble here is that the Lipschitz coefficient is rather large, order d2.

slide18
(2) Concentration using average Lipschitz coefficient (Kim-Vu 98):

Where E is the expectation of Y, E’ is the average Lipschitz coefficient, and k is the degree of Y (k=3). This gives d=o(n1/10).

(3)For d= o(n1/3) we need a stronger version (V.00)

and many extra combinatorial arguments.

slide19
What happen beyond n1/3 ?

We conjecture that the probability is still asymptotically uniform for most d-regular graph G. Maybe some graph properties of G need to be exploited.

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