Graphon
In graph theory and statistics, a graphon is a symmetric measurable function, that is important in the study of dense graphs. Graphons arise both as a natural notion for the limit of a sequence of dense graphs, and as the fundamental defining objects of exchangeable random graph models. Graphons are tied to dense graphs by the following pair of observations: the random graph models defined by graphons give rise to dense graphs almost surely, and, by the regularity lemma, graphons capture the structure of arbitrary large dense graphs.
Statistical formulation
A graphon is a symmetric measurable function. Usually a graphon is understood as defining an exchangeable random graph model according to the following scheme:- Each vertex of the graph is assigned an independent random value
- Edge is independently included in the graph with probability.
The model based off a fixed graphon is sometimes denoted,
by analogy with the Erdős–Rényi model of random graphs.
A graph generated from a graphon in this way is called a -random graph.
It follows from this definition and the law of large numbers that, if, exchangeable random graph models are dense almost surely.
Examples
The simplest example of a graphon is for some constant. In this case the associated exchangeable random graph model is the Erdős–Rényi model that includes each edge independently with probability.If we instead start with a graphon that is piecewise constant by:
- dividing the unit square into blocks, and
- setting equal on the block,
community stochastic block model, a generalization of the Erdős–Rényi model.
We can interpret this as a random graph model consisting of distinct Erdős–Rényi graphs with parameters respectively, with bigraphs between them where each possible edge between blocks and is included independently with probability.
Many other popular random graph models can be understood as exchangeable random graph models defined by some graphon, a detailed survey is included in Orbanz and Roy.
Jointly exchangeable adjacency matrices
A random graph of size can be represented as a random adjacency matrix. In order to impose consistency between random graphs of different sizes it is natural to study the sequence of adjacency matrices arising as the upper-left sub-matrices of some infinite array of random variables; this allows us to generate by adding a node to and sampling the edges for. With this perspective, random graphs are defined as random infinite symmetric arrays.Following the fundamental importance of exchangeable sequences in classical probability, it is natural to look for an analogous notion in the random graph setting. One such notion is given by jointly exchangeable matrices; i.e. random matrices satisfying
for all permutations of the natural numbers, where means equal in distribution. Intuitively, this condition means that the distribution of the random graph is unchanged by a relabeling of its vertices: that is, the labels of the vertices carry no information.
There is a representation theorem for jointly exchangeable random adjacency matrices, analogous to de Finetti’s representation theorem for exchangeable sequences. This is a special case of the Aldous–Hoover theorem for jointly exchangeable arrays and, in this setting, asserts that the random matrix is generated by:
- Sample independently
- independently at random with probability
Graphon estimation
Due to identifiability issues, it is impossible to estimate either the graphon function or the node latent positions and there are two main directions of graphon estimation. One direction aims at estimating up to an equivalence class, or estimate the probability matrix induced by.Analytic formulation
Any graph on vertices can be identified with its adjacency matrix.This matrix corresponds to a stepfunction,
defined by partitioning into intervals
such that has interior
and for each, setting equal to the
entry of.
This function is the associated graphon of the graph.
In general, if we have a sequence of graphs where the number of vertices of goes to infinity, we can analyze the
limiting behavior of the sequence by considering the limiting behavior of the functions.
If these graphs converge, then we expect the limit of these graphs to correspond to the limit of these associated functions.
This motivates the definition of a graphon as a symmetric measurable function which
captures the notion of a limit of a sequence of graphs.
It turns out that for sequences of dense graphs, several apparently distinct notions of convergence are equivalent and under all of them the natural limit object is a graphon.
Examples
Example 1: Take a random sequence Erdős–Rényi graphs with some fixed parameter.Intuitively, as tends to infinity, the limit of this sequence of graphs is determined solely by edge density of these graphs.
In the space of graphons, it turns out that such a sequence converges almost surely to the constant, which captures the above intuition.
Example 2:
Take the sequence of half-graphs, defined by taking to be the bipartite graph on vertices and such that is adjacent to precisely when. If the vertices are listed in the presented order, then
the adjacency matrix has two corners of "half square" block matrices filled with ones, with the rest of the entries equal to zero.
For example, the adjacency matrix of is given by
As gets large, these corners of ones "smooth" out.
Matching this intuition, the sequence converges to the half-graphon defined by when and otherwise.
Example 3:
Take the sequence of complete bipartite graphs with equal sized parts.
If we order the vertices by placing all vertices in one part at the beginning
and placing the vertices of the other part at the end,
the adjacency matrix of looks like a block off-diagonal matrix, with two blocks of ones and two blocks of zeros.
For example, the adjacency matrix of is given by
As gets larger, this block structure of the adjacency matrix remains constant,
so that this sequence of graphs converges to a "complete bipartite" graphon
defined by whenever and, and setting otherwise.
Example 4:
Consider the sequence from the previous example again.
If we instead order vertices by alternating between parts,
the adjacency matrix has a chessboard structure of zeros and ones.
For example, under this ordering, the adjacency matrix of is given by
As gets larger,
the adjacency matrices become a finer and finer chessboard.
Despite this behavior, we still want the limit of to be unique and result in the graphon from example 4.
This means when we formally define convergence for a sequence of graphs, the definition of a limit should be agnostic to relabelings of the vertices.
Example 5: Take a random sequence of -random graphs by drawing for some fixed graphon.
Then just like in the first example from this section, it turns out that converges to almost surely.
Example 6:
Given graph with associated graphon, we can recover graph theoretic properties and parameters of by integrating transformations of.
For example, the edge density of is given by the integral
This is because is -valued, and each edge
in
corresponds to a region
of area where equals.
Similar reasoning shows that the number of triangles in is equal to
Notions of convergence
There are many different ways to measure the distance between two graphs.If we are interested in metrics that "preserve" extremal properties of graphs,
then we should restrict our attention to metrics that identify random graphs as similar.
For example, if we
randomly draw two graphs independently from an Erdős–Rényi model for some fixed, the distance between these two graphs under a "reasonable" metric should be close to zero with high probability for large.
There are two natural metrics that behave well on dense random graphs, in this sense.
The first is a sampling metric, which says two graphs are close if their distributions of subgraphs are close.
The second is an edge discrepancy metric, which says two graphs are close when their edge densities are close on all their corresponding subsets of vertices.
Miraculously, a sequence of graphs converges when with respect to one distance precisely when it converges with respect to the other.
Moreover, the limit objects under both distances turn out to graphons.
The equivalence of these two notions of convergence mirrors how various notions of quasirandom graphs are equivalent.
Subgraph counts
One way to measure the distance between two graphs and is to compare their relative subgraph counts.That is, for each graph we can compare the number of copies of in and in.
If these numbers are close for every graph, then
intuitively and are similar looking graphs.
Homomorphism densities
Rather than dealing directly with subgraphs, it turns out to beeasier to work with graph homomorphisms.
This is fine when dealing with large, dense graphs, since in this scenario
the number of subgraphs and the number of graph homomorphisms from a fixed graph are asymptotically equal.
Given two graphs and, the
homomorphism density of in is defined to be the number of graph homomorphisms from to .
In other words, is the probability a randomly chosen map from the vertices of to the vertices of sends adjacent vertices in to adjacent vertices in.
Graphons offer a simple way to compute homomorphism densities.
Indeed, given a graph with associated graphon and another, we have
where the integral is multidimensional, taken over the unit hypercube.
This follows from the definition of an associated graphon, by considering when the above integrand is equal to.
We can then extend the definition of homomorphism density to arbitrary graphons, by using the same integral and defining
for any graph.
Limits in terms of homomorphism density
Given this setup, we say a sequence of graphs converges if for every fixed graph, the sequence of homomorphism densities converges.Although not evident from the definition alone, if converges in this sense, then there always exists a graphon such that for every graph, we have
simultaneously.
Cut distance
Take two graphs and on the same vertex set.Because these graphs share the same vertices,
one way to measure their distance is to restrict to subsets of the vertex set, and for each such pair subsets compare the number of edges from to in to the number of edges between and in.
If these numbers are similar for every pair of subsets, then that suggests and are similar graphs.
To formalize this, for any symmetric, measurable function, define
the cut norm of to be the quantity
taken over all measurable subsets of the unit interval.
This norm captures our earlier notion of distance, because for two graphs and with same vertex set of size, the cut norm with the associated graphons
lets us compute the maximum discrepancy of the edge densities between and.
Note that this definition can still be used even when the graphs being compared do not share a vertex set.
This distance measure still has one major limitation: it can assign nonzero distance to two isomorphic graphs.
To make sure isomorphic graphs have distance zero, we should compute the minimum cut norm over all possible "relabellings" of the vertices.
This motivates the definition of the cut distance between two graphons and to be
where is the composition of with the map, and the infimum is taken over all measure-preserving bijections from the unit interval to itself.
The cut distance between two graphs is defined to be the cut distance between their associated graphons.
As a metric space
To make the cut-distance into a metric, we take the set of all graphons and identify two graphons whenever.The resulting space of graphons is denoted, and together with forms a metric space.
This space turns out to be compact.
Moreover, it contains the set of all finite graphs as a dense subset.
Here, graphs are being identified as -valued step functions on the unit square.
These observations show that the space of graphons is a completion of the space of graphs with respect to the cut distance.
Applications
Regularity lemma
Using compactness of the space of graphons, one can prove stronger versions of Szemerédi's regularity lemma.Sidorenko's conjecture
The analytic nature of graphons allows greater flexibility in attacking inequalities related to homomorphisms.For example,
Sidorenko's conjecture is a major open problem in extremal graph theory, which asserts that for any graph on vertices with average degree and bipartite graph on vertices and edges, the number of homomorphisms from to is at least.
Since this is quantity is the expected number of labeled subgraphs of in a random graph,
the conjecture can be interpreted as the claim
that for any bipartite graph, the random graph achieves the minimum number of copies of over all graphs with some fixed edge density.
Many approaches to Sidorenko's conjecture formulate the problem as an integral inequality on graphons, which then allows the problem to be attacked using other analytical approaches.
Generalizations
Graphons are naturally associated with dense simple graphs.There are extensions of this model to dense directed weighted graphs, often referred to as decorated graphons. There are also recent extensions to the sparse graph regime, from both the perspective of random graph models and graph limit theory.