The counting lemmas this article discusses are statements in combinatorics and graph theory. The first one extracts information from  -regular pairs of subsets of vertices in a graph
-regular pairs of subsets of vertices in a graph  , in order to guarantee patterns in the entire graph; more explicitly, these patterns correspond to the count of copies of a certain graph
, in order to guarantee patterns in the entire graph; more explicitly, these patterns correspond to the count of copies of a certain graph  in
 in  . The second counting lemma provides a similar yet more general notion on the space of graphons, in which a scalar of the cut distance between two graphs is correlated to the homomorphism density between them and
. The second counting lemma provides a similar yet more general notion on the space of graphons, in which a scalar of the cut distance between two graphs is correlated to the homomorphism density between them and  .
.
Graph embedding version of counting lemma
Whenever we have an  -regular pair of subsets of vertices
-regular pair of subsets of vertices  in a graph
 in a graph  , we can interpret this in the following way: the bipartite graph,
, we can interpret this in the following way: the bipartite graph,  , which has density
, which has density  , is close to being a random bipartite graph in which every edge appears with probability
, is close to being a random bipartite graph in which every edge appears with probability  , with some
, with some  error.
 error.
In a setting where we have several clusters of vertices, some of the pairs between these clusters being  -regular, we would expect the count of small, or local patterns, to be roughly equal to the count of such patterns in a random graph. These small patterns can be, for instance, the number of graph embeddings of some
-regular, we would expect the count of small, or local patterns, to be roughly equal to the count of such patterns in a random graph. These small patterns can be, for instance, the number of graph embeddings of some  in
 in  , or more specifically, the number of copies of
, or more specifically, the number of copies of  in
 in  formed by taking one vertex in each vertex cluster.
 formed by taking one vertex in each vertex cluster.
The above intuition works, yet there are several important conditions that must be satisfied in order to have a complete statement of the theorem; for instance,  the pairwise densities are at least  , the cluster sizes are at least
, the cluster sizes are at least  , and
 , and  . Being more careful of these details, the statement of the graph counting lemma is as follows:
. Being more careful of these details, the statement of the graph counting lemma is as follows:
Statement of the theorem
If  is a graph with vertices
 is a graph with vertices  and
 and  edges, and
 edges, and  is a graph with (not necessarily disjoint) vertex subsets
 is a graph with (not necessarily disjoint) vertex subsets  , such that
, such that  for all
 for all  and for every edge
 and for every edge  of
 of  the pair
 the pair  is
 is  -regular with density
-regular with density  and
 and  ,  then
,  then  contains  at  least
 contains  at  least  many copies of
 many copies of  with the copy of vertex
 with the copy of vertex  in
 in  .
. 
This theorem is a generalization of the triangle counting lemma, which states the above but with  :
:
Triangle counting Lemma
Let  be a graph on
 be a graph on  vertices, and let
 vertices, and let  be subsets of
 be subsets of  which are pairwise
 which are pairwise  -regular, and suppose the edge densities
-regular, and suppose the edge densities  are all at least
 are all at least  . Then the number of triples
. Then the number of triples  such that
 such that  form a triangle in
 form a triangle in  is at least
 is at least
Proof of triangle counting lemma:
Since  is a regular pair, less than
 is a regular pair, less than  of the vertices in
 of the vertices in  have fewer than
 have fewer than  neighbors in
 neighbors in  ; otherwise, this set of vertices from
; otherwise, this set of vertices from  along with its neighbors in
 along with its neighbors in  would witness irregularity of
 would witness irregularity of  , a contradiction. Intuitively, we are saying that not too many vertices in
, a contradiction. Intuitively, we are saying that not too many vertices in  can have a small degree in
 can have a small degree in  .
.
By an analogous argument in the pair  , less than
, less than  of the vertices in
 of the vertices in  have fewer than
 have fewer than  neighbors in
 neighbors in  . Combining these two subsets of
. Combining these two subsets of  and taking their complement, we obtain a subset
 and taking their complement, we obtain a subset  of size at least
 of size at least  such that every vertex
 such that every vertex  has at least
 has at least  neighbors in
 neighbors in  and at least
 and at least  neighbors in
 neighbors in  .
.
We also know that  , and that
, and that  is an
 is an  -regular pair; therefore, the density between the neighborhood of
-regular pair; therefore, the density between the neighborhood of  in
 in  and the neighborhood of
 and the neighborhood of  in
 in  is at least
 is at least  , because by regularity it is
, because by regularity it is  -close to the actual density between
-close to the actual density between  and
 and  .
.
Summing up, for each of these at least  vertices
 vertices  , there are at least
, there are at least  choices of edges between the neighborhood of
 choices of edges between the neighborhood of  in
 in  and the neighborhood of
 and the neighborhood of  in
 in  . From there we can conclude this proof.
. From there we can conclude this proof.
Idea of proof of graph counting lemma:The general proof of the graph counting lemma extends this argument through a greedy embedding strategy; namely, vertices of  are embedded in the graph one by one, by using the regularity condition so as to be able to keep a sufficiently large set of vertices in which we could embed the next vertex.[1]
 are embedded in the graph one by one, by using the regularity condition so as to be able to keep a sufficiently large set of vertices in which we could embed the next vertex.[1]
Graphon version of counting lemma
The space  of graphons is given the structure of a metric space where the metric is the cut distance
 of graphons is given the structure of a metric space where the metric is the cut distance  . The following lemma is an important step in order to prove that
. The following lemma is an important step in order to prove that  is a compact metric space. Intuitively, it says that for a graph
 is a compact metric space. Intuitively, it says that for a graph  , the homomorphism densities of two graphons with respect to this graph have to be close  (this bound depending on the number of edges
, the homomorphism densities of two graphons with respect to this graph have to be close  (this bound depending on the number of edges  ) if the graphons are close in terms of cut distance.
) if the graphons are close in terms of cut distance.
Definition (cut norm).
The cut norm of ![{\displaystyle W:[0,1]^{2}\to \mathbb {R} }](./_assets_/1e0f6c0cdba0f680b28174025d1ce009d67f1be4.svg) is defined as
 is defined as ![{\displaystyle \|W\|_{\square }=\sup _{S,T\subseteq [0,1]}\left|\int _{S\times T}W\right|}](./_assets_/eefa829473576e5ac8c14803bb658e7702ad1833.svg) , where
, where  and
 and  are measurable sets.
 are measurable sets.
Definition (cut distance).
The cut distance is defined as  , where
, where  represents
 represents  for a measure-preserving bijection
 for a measure-preserving bijection  .
.
Graphon Counting Lemma
For graphons  and graph
 and graph  , we have
, we have  , where
, where  denotes the number of edges of graph
 denotes the number of edges of graph  .
.
Proof of the graphon counting lemma:
It suffices to prove  Indeed, by considering the above, with the right hand side expression having a factor
Indeed, by considering the above, with the right hand side expression having a factor  instead of
 instead of  , and taking the infimum of the  over all measure-preserving bijections
, and taking the infimum of the  over all measure-preserving bijections  , we obtain the desired result.
, we obtain the desired result.
Step 1: Reformulation. We prove a reformulation of the cut norm, which is by definition the left hand side of the following equality. The supremum in the right hand side is taken among measurable functions  and
 and  :
:![{\displaystyle \sup _{S,T\subseteq [0,1]}\left|\int _{S\times T}W\right|=\sup _{u,v:[0,1]\rightarrow [0,1]}\left|\int _{[0,1]^{2}}W(x,y)u(x)v(y)dxdy\right|.}](./_assets_/d3547563f5f4c4821aaf0686c82daa57998c6d47.svg) 
Here's the reason for the above to hold: By taking  and
 and  , we note that the left hand side is less than or equal than the right hand side. The right hand side is less than or equal than the left hand side by bilinearity of the integrand in
, we note that the left hand side is less than or equal than the right hand side. The right hand side is less than or equal than the left hand side by bilinearity of the integrand in  , and by the fact that the extrema are attained for
, and by the fact that the extrema are attained for  taking values at
 taking values at  or
 or  .
.
Step 2: Proof for  . In the case that
. In the case that  , we observe that
, we observe that
![{\displaystyle {\begin{aligned}t(K_{3},W)-t(K_{3},U)&=\int _{[0,1]^{3}}((W(x,y)W(x,z)W(y,z)-U(x,y)U(x,z)U(y,z))dxdydz\\&=\int _{[0,1]^{3}}(W-U)(x,y)W(x,z)W(y,z)dxdydz\\&\qquad +\int _{[0,1]^{3}}U(x,y)(W-U)(x,z)W(y,z)dxdydz\\&\qquad +\int _{[0,1]^{3}}U(x,y)U(x,z)(W-U)(y,z)dxdydz.\end{aligned}}}](./_assets_/b49acca900eecb882ad80b8848e383bb6a5917cb.svg) 
By Step 1, we have that for a fixed  that
 that
![{\displaystyle \left|\int _{[0,1]^{2}}(W-U)(x,y)W(x,z)W(y,z)dxdy\right|\leq \|W-U\|_{\square }}](./_assets_/d6ab1171025e7f79ab123c54aa306861bc3295d4.svg)
Therefore, when integrating over all ![{\displaystyle z\in [0,1]}](./_assets_/f20fead0085cbd4473680e23f8353908a40ab312.svg) we get that
  we get that 
![{\displaystyle \left|\int _{[0,1]^{3}}(W-U)(x,y)W(x,z)W(y,z)dxdydz\right|\leq \|W-U\|_{\square }}](./_assets_/e09ef6315d50ee21cdf7a593b4fbb98f956069dd.svg)
Using this bound on each of the three summands, we get that the whole sum is bounded by  .
Step 3: General case. For a general graph
.
Step 3: General case. For a general graph  , we need the following lemma to make everything more convenient:
, we need the following lemma to make everything more convenient:
Lemma.
The following expression holds:
The above lemma follows from a straightforward expansion of the right hand side. Then, by the triangle inequality of norm, we have the following 
Here, each absolute value term in the sum is bounded by  the cut norm  if we fix all the variables except for
 if we fix all the variables except for  and
 and  for each
 for each  -th term, altogether implying that
-th term, altogether implying that  . This finishes the proof.
. This finishes the proof.
See also
References
- ^ Conlon, Fox, David, Jacob. "Graph Removal Lemmas" (PDF). David Conlon's webpage. Archived (PDF) from the original on 2013-10-01.{{cite web}}:  CS1 maint: multiple names: authors list (link)