1. Introduction
An infinite family of -biregular graphs with is called a unique neighbour expander family if there exists such that, for every n and every set of left-side vertices of size , there exists a unique neighbour of S in , namely, a vertex in that is connected to exactly one vertex in S. We only require that sets of left vertices have unique neighbours, and, arbitrarily, small right-side sets may have no unique neighbour.
Alon and Capalbo [
1] constructed several explicit families of unique neighbour expanders, via an elegant composition of a Ramanujan graph and a gadget. They constructed three families of general (non-bipartite) graphs in which all small sets have unique neighbours, and one family of slightly unbalanced bipartite graphs, where small sets on the left have unique neighbours on the right. In their construction, the left side is
times bigger than the right side. The more imbalanced the graph, the harder it is for small left-hand side sets to expand into the right-hand side. Capalbo et al. [
2] constructed arbitrarily unbalanced bipartite graphs that are lossless expanders, a notion strictly stronger than unique neighbour expansion. Their construction is based on a sequence of somewhat involved composition steps using randomness conductors.
Our main theorem is an efficient construction of an infinite family of bipartite unique neighbour expanders for any constant imbalance , and any sufficiently large left-regularity degrees of a specific form.
Theorem 1. There is a function such that, for every integer and real number , if is a prime power and is an integer, then there is an infinite family of -biregular unique neighbour expanders. The family is constructible in polynomial time in the size of the graph.
The theorem is proven in
Section 6.2, and provides a way to compute
. Here are some computed values of
for several values of
:
It should be noted that increases with , reflecting the fact that constructions with larger (namely, more imbalanced sides) are harder to come by, and require larger degrees.
One of the prominent uses of bipartite expanders in general and bipartite unique neighbour expanders in particular, and the motivation for this work, is the construction of error-correcting codes. The work of Sipser and Spielman [
3] constructs a linear error-correcting code from a bipartite unique neighbour expander, simply by looking at the adjacency matrix of the graph as a parity check matrix for the code. It is immediately obvious that the parameter
from the unique neighbour property is a lower bound on the distance of the code.
Recently, there has also been a line of work constructing quantum LDPC codes from expanders, and a construction using lossless expansion (a strengthening of unique neighbour expansion) was introduced in [
4]. Following our work, several new works [
5,
6,
7,
8] were published, which give either similar constructions or even the stronger notion of lossless expansion (thereby simplifying and reproducing the results of [
2]). All of these results (including ours) only discuss the expansion of subsets of
L (and not of
R) and, therefore, fall short from being used in the qLDPC construction of [
4].
The construction uses an infinite family of bipartite Ramanujan graphs, namely graphs whose nontrivial spectrum is contained in the spectrum of the
-biregular tree (see
Section 3 for details). We constructed the unique neighbour expander family by taking a family of bipartite Ramanujan graphs and combining them with a fixed-size graph (a “gadget”), with a good unique neighbour property (small sets have unique neighbours), whose existence is shown via the probabilistic method (Lemma 6). The combination is done as follows. We first place a copy of the gadget for every right-side vertex of the Ramanujan graph. The vertex is replaced by the right side of the gadget, and its neighbours are identified with the left side of the gadget. The gadget is used to route the neighbours of each left-side vertex in the Ramanujan graph to its neighbours in the product graph.
The expansion in the product graph comes from the unique neighbour expansion of the gadget, together with low-degree vertices in induced subgraphs of the Ramanujan graph. The latter is guaranteed to exist thanks to the following (new) bound on the average degree of induced subgraphs of bipartite Ramanujan graphs, which may be of independent interest.
Theorem 2. Let be a -biregular Ramanujan graph, and let .
Then, there exists ,
which depends only on ,
such that, for every of size ,
the set of the neighbours of S satisfies The theorem shows that every small set on the left side admits neighbours on the right side, with very few neighbours in the induced subgraph. The proof involves the recursive analysis of non-backtracking paths. Interestingly, the recursion has a nice solution only when the graph is Ramanujan. It is unclear whether this method can be extended to “nearly-Ramanujan” graphs.
We used the theorem in the following way. Given a small enough subset of left-side vertices, the theorem guarantees that it has an “almost” unique neighbour, in the sense that its degree in the induced subgraph is low (namely, it is connected to few vertices in our subset). That implies, via our construction, a small subset of vertices in a copy of the gadget; the latter has a unique neighbour in the gadget, which gives (through Lemma 7) a unique neighbour in our product graph.
Even though Ramanujan graphs are the best spectral expanders one can hope for, an efficient construction of Ramanujan graphs (be them bipartite or not) does not immediately imply that we can construct unique neighbour expanders. In the
d-regular case, Kahale shows [
9] (Thm 5.2) that there are nearly-Ramanujan graphs with an expansion of
at most, which is not enough for unique neighbour expansion. In fact, recently, Kamber and Kaufman [
10] proved that some Ramanujan graphs strongly fail to have unique neighbour expansion, by giving explicit constructions of arbitrarily small sets that do not admit a unique neighbour.
As mentioned previously, the graph product we defined requires a fixed-size gadget, whose proof of existence is not constructive. In principle, such a gadget could be found by an exhaustive search, since we are working in a constant-sized search space. The gadget’s size in our construction is at least cubic in
q, so an exhaustive search is impractical, even for small values of
q. Unfortunately, we know of no efficient construction methods for a gadget with the required parameters, and were unable to find a single working example of such a gadget. Thus, currently, our construction does not give any concrete new error-correcting codes, but rather only a scheme for the construction of such codes. It is possible that the graph sampling method presented in [
11] can be used to construct fixed-size gadgets more efficiently.
The rest of this work is organised as follows. In
Section 2, we survey some of the uses of unique neighbour expanders, and mention known constructions of such graphs.
Section 3 provides basic definitions and results. Our main technical tool, which asserts the low induced degree in bipartite Ramanujan graphs, is stated and proven in
Section 4. We prove the existence of a fixed-size gadget with good unique neighbour expansion properties in
Section 5. In
Section 6, we define the way we use the Ramanujan graphs and the gadget to construct bipartite unique neighbour expanders, and by that, prove Theorem 1.
2. Related Work
As mentioned earlier, unique neighbour expanders are motivated by the construction of error-correcting codes. In a seminal paper [
3], Sipser and Spielman showed two constructions of expander codes. The first construction was obtained by simply using the adjacency matrix of a unique neighbour expander as a parity check matrix. The second was a Tanner code that is based on two components: a spectral expander graph and a constant-sized code
.
Our construction imitates the ideas underlying the second construction. We took a spectral (Ramanujan) expander and combined it with a small gadget graph in a way that resembles a constant-sized code . Essentially, we put the local code as a gadget under the hood of our new expander construction.
If one plugs in our expander graphs into the first construction of codes from [
3], these codes can be viewed as expander codes from the
second construction, if we re-interpret the gadget as an inner code. The new point of view allows us to rely on slightly weaker features of the code, replacing odd neighbour expansion with unique neighbour expansion.
We give full details of this graph product in
Section 6.1.
Unique neighbour expansion is also important for error-correcting codes with additional features. In [
12,
13], it is shown that codes constructed from unique neighbour expanders are weakly smooth and can be used to construct robustly testable codes. This property, in turn, is important for the composition of locally testable codes, and, more recently, for quantum LDPC codes (see [
14,
15]). As mentioned earlier, in the work of Hsieh and Lin [
4], lossless expanders (which are a strengthening of unique neighbour expanders) are used to construct quantum LDPC codes with good rate and distance.
The uses of unique neighbour expanders are not limited to error-correcting codes: for example, such graphs may be used in the context of non-blocking networks, where it is required to connect several input–output terminals via paths in a non-intersecting fashion. Arora et al. [
16] used graphs with expansion beyond the
barrier to establish the existence of unique neighbours in the graph, which are useful in finding input–output paths in the online settings. Roughly speaking, when routing a set of input–output pairs, the algorithm can use all unique neighbours freely since they are guaranteed not to interfere with any other paths. Pippenger [
17] used explicit constructions of spectral expanders in order to solve a similar problem, in the case where the route planning is computed locally. There, the spectral expansion of a graph was proven to imply a combinatorial expansion, in a similar way to our Theorem 2.
Another use for unique neighbour expanders is for load-balancing problems, such as the token distribution problem described in [
18], and the similar pebble distribution problem, briefly discussed in [
1]. In the latter, pebbles are placed arbitrarily on vertices of a graph, and need to be distributed via the edges of the graph, such that no vertex has more than one pebble. Given that the total number of pebbles is small and that the graph has the unique neighbour property, we have an efficient parallel algorithm for redistributing the pebbles.
Alon and Capalbo [
1] constructed several families of unique neighbour expanders; one of them is a family of bipartite graphs whose left side is
times bigger than the right side. Similar to the construction presented in this work, each graph in the constructed family is a combination of a Ramanujan graph and a fixed graph. These graphs are not (bi)regular, but their degrees are bounded by a constant. Becker [
19] used a different family of 8-regular Ramanujan graphs in order to construct a family of (non-bipartite) unique neighbour expanders, with the additional property that each graph in the family is a Cayley graph.
A slightly different approach to constructing bipartite graphs uses randomness conductors. At a high level, our construction is not much different, since it also uses a large spectral expander combined with a constant-sized gadget. However, conceptually, the difference is that the gadgets required in [
2] are more involved. Randomness conductors are functions that receive a bitstring with some entropy (according to some measure of entropy) and a uniformly random bitstring, and output a bitstring with certain guarantees on its entropy. Some conductors can be constructed explicitly via a spectral method, and Capalbo et al. [
2] combined them in a zig-zag-like fashion in order to construct an infinite family of
bipartite lossless expanders, namely bipartite graphs with fixed left-regularity
c, where small enough sets contained in the left side have at least
neighbours on the right side. These graphs are trivially unique neighbour expanders, since a simple counting argument shows that if a set expands by a factor of more than
, then it has unique neighbours.
4. Vertex Expansion in Biregular Ramanujan Graphs
Our main technical tool is the following theorem showing that bipartite Ramanujan graphs exhibit excellent left-to-right expansion. As we will see shortly, this is an improvement of the expander mixing lemma. We restate the theorem for convenience.
Theorem 4. Let be a -biregular Ramanujan graph, and let .
Then, there exists ,
which depends only on ,
such that, for every of size ,
the set of the neighbours of S satisfies We note that the quantity on the left-hand side of the inequality can be interpreted as follows. Let us observe the bipartite graph induced by taking the vertices
S on the left and
on the right. Since every left vertex has
c outgoing edges, the total number of edges in the induced subgraph is
. This means that the expression on the left-hand side of the inequality is exactly the average degree of the right side of the induced subgraph.Interestingly, the bound in this theorem is strictly stronger than what we would obtain from just applying the expander mixing lemma, which amounts to
See Claim 1 for details. The fact that we improve upon the expander mixing lemma is perhaps not surprising, since our analysis is based on enumerating non-backtracking paths, and not just on the magnitude of the second-largest eigenvalue. We also use lower bounds on the magnitude of all nontrivial eigenvalues, whereas the expander mixing lemma uses just upper bounds.
4.1. Comparison to Known Bounds
As noted above, Theorem 2 is an improvement of the bound that the expander mixing lemma gives in similar settings. For reference, we state and prove the expander mixing lemma for bipartite Ramanujan graphs. We recall that if G is a -biregular Ramanujan graph, then its nontrivial eigenvalues satisfy . The expander mixing lemma only uses the upper bound.
Claim 1 (Expander Mixing Lemma for Bipartite Ramanujan Graphs)
. Let be a
-biregular Ramanujan graph, and let . Then, there exists such that, for every of size , the neighbourhood of S satisfies Proof. The expander mixing lemma for biregular graphs says that, for every
,
, we have
where
is the second-largest eigenvalue of the adjacency operator associated with
G, as defined in item 3 of
Section 3.1. So, the largest eigenvalue is
(we look at the spectrun of a matrix indexed by vertices of
G, where
if and only if
are neighbours in
G. We obtain the largest eigenvalue
with an eigenvector that is
on the left side of
G, and
on the right side of
G.).
Picking
means all edges coming out from
S are in the cut, namely,
. Plugging that in gives
Multiplying both sides by
gives
where we also used the fact that
.
Let us assume that
. Then, we can upper bound
by
and so, we have
We square (
1) and plug in the last inequality to obtain
We recall that
G is bipartite Ramanujan, so
. We use that and rearrange as follows:
The claim is proven by noting that there is some , such that for every , namely, whenever . □
Kahale proved ([
9], Thm 4.2) that in
d-regular Ramanujan graphs (not necessarily bipartite), small induced subgraphs have an average degree of
at most. Interestingly, this result can be deduced almost immediately from Theorem 2. This is due to the following lemma, proven in
Appendix A, which asserts that the
edge–vertex incidence graph (see [
3]) of a
d-regular Ramanujan graph is a
-biregular Ramanujan graph:
Lemma 1. Let G be a d-regular Ramanujan graph, and its edge–vertex incidence graph. Then, is a -biregular Ramanujan graph.
We state and prove Kahale’s bound, but we did not use it in our construction.
Corollary 1. Let be a d-regular Ramanujan graph, and let . Then, there exists , such that, for every induced subgraph with vertices at most, the average degree of S is, at most, Proof. Let
be a
d-regular Ramanujan graph and
. We define
as the edge–vertex incidence graph, namely,
,
, and, for every edge
in
G, we have the two edges
and
in
. Since the degree of every vertex in
G is
d, and, since every edge has two endpoints, we have that
is a
-biregular graph. Lemma 1 asserts that
is Ramanujan in the bipartite sense. By Theorem 2, there exists
such that, if
is of size
at most, then
A subgraph of G satisfies that is a subset of left-side vertices in , is a subset of right-side vertices in , and (because if an edge is in the subgraph, then both of its endpoints are in the subgraph, and we assume that the subgraph does not contain an isolated vertex). Therefore, if is sufficiently small, namely, if , then, by Theorem 2, the average degree of is bounded by .
We add that if we wish to find a bound for the number of vertices, we note that . So, every induced subgraph with no more than vertices will satisfy the required average degree bound. □
4.2. Proof of Theorem 2
Theorem 2 is proven by enumerating non-backtracking paths. A non-backtracking path of length ℓ is a sequence of edges , such that, for every i, and .
For a bipartite graph
G and a subset
S of left-side vertices, we define
to be the number of all non-backtracking paths whose left-side vertices are all in
S, and
to be the number of non-backtracking paths whose first and last left-side vertices are in
S. Clearly,
, as paths of the latter type may leave
(before re-entering
S at the last step). We use a lower bound on
due to [
30]:
Lemma 2. For every undirected bipartite graph and integer ℓ, it holds thatwhere are the average degrees of the left and right sides of G, respectively. We state and prove an upper bound on :
Lemma 3. Let G be a -biregular Ramanujan graph with n vertices on the left side, and S a subset of the left side. Then, for every integer ℓprovided that S is small enough: Before proving the upper bound, we will show how these bounds can be combined to obtain Theorem 2.
Proof of Theorem 2. Let
ℓ be an integer to be determined later and
a sufficiently small subset (where sufficiently small means (
2)). We denote by
the neighbours of
S. The subgraph induced on
has
edges, with all left degrees
c and average right degree
.
Chaining the inequalities in Lemmas 2 and 3, we have
Simplifying, we obtain
Since
, we have that
, so
; hence, for a fixed
, there exists a constant
ℓ (that depends only on
), such that
; this
ℓ determines, via inequality (
2), a fixed
, such that, whenever
, we have
□
We proceed to prove Lemma 3.
For a bipartite graph and an integer ℓ, we define and as operators corresponding to non-backtracking paths of length ℓ, that is, is an matrix whose entry, when are left-side vertices, is the number of non-backtracking paths of length ℓ from u to v. The other three operators are defined similarly.
Let
M be the operator corresponding to a single step from the right side
G to the left side of
G, namely,
M has
rows and
columns, with
counting the number of edges between
and
in
G. Then, the following recursive formulae hold for every integer
:
The first formula is explained as follows. Every non-backtracking path from R to L of length is composed of a non-backtracking path from L to L of length ℓ plus an extra step (that is the factor.) The opposite is true, except for paths counted in that do backtrack, namely those made of a non-backtracking path of length , and walking back and forth along the same edge. There are ways to choose that edge (since it cannot be the one that was last in the path of length , otherwise, it would not be counted in ), so we need to subtract . The rest of the equations are explained in an analogue way.
Due to symmetry, we have
Furthermore, since the graph is bipartite we have
for every integer
ℓ. These equations yield a recursive formula for
, with the following initial conditions:
The following lemma, proven in
Appendix A, suggests a way to find a non-recursive formula for
, given such linear recursive relations with fixed coefficients.
Lemma 4. Let be a series defined via a second-order linear recurrence with fixed coefficients :We assume are (real or complex) roots of the characteristic polynomial . Then, there are , which depend on the initial conditions , such thatfor every . If the characteristic polynomial has a single root λ of multiplicity 2, then there are , such thatfor every . We use the lemma to bound the eigenvalues of given bounds on the spectrum of the biregular graph.
Lemma 5. Let G be a -biregular graph. Then, there is a sequence of polynomials with integer coefficients such that, for every eigenvalue v of G with eigenvalue λ, is an eigenvalue of , and, moreover, for every , ifthen Proof. The recursive formulae proven above (
3) suggest that there is a series of polynomials
with integer coefficients such that
. It should be noted that the graph’s adjacency matrix is
Furthermore, if
is an eigenpair of
G, then
is an eigenpair of
This shows that
is an eigenvalue of
whenever
is an eigenvalue of
G. The converse is also true.
The formulae (
3) can be transformed so as to convey that
satisfies the following equations:
for all
. Setting
gives an equation involving
and
. We can solve this equation for
and obtain a simpler description of the initial conditions:
for all
.
We fix some
t that satisfies (
4), namely,
We first deal with the case where
, and, later, we will consider the edge cases where
t is one of the endpoints of the segment or 0. Let us write
. We have that, for this fixed
x, the series
satisfies a second-order linear recurrence with fixed coefficients. Using Lemma 4, we conclude that there are functions
and
that depend only on
, and
d, such that
for every
n.
In order to find
, we solve for
the characteristic polynomial, namely, the following quadratic equation derived from (7):
to obtain
where
Using the initial values for
from (
6), and plugging back into (
8), we obtain the equations
whose solution is
We check when
by solving (
9) for
x:
We see that
is quadratic in
x and has roots at
. This gives a nice factorization of
:
We recall that, for the
x we fixed, we have
, so the first term in the product is negative and the second term is positive, so
, and
are complex numbers (conjugate to one another), with magnitude
A very similar calculation shows that
are conjugates with magnitude
This finishes the proof for all such
xs:
We keep in mind that x is fixed, so the expression is smaller than for large enough ℓ.
We are left with the cases for :
□
Bounds on the spectrum of give bounds on the number of non-backtracking paths completely contained in a small set, hence, give Lemma 3.
Proof of Lemma 3. We recall that counts the number of non-backtracking paths of length that start and end in S, so, by the definition of the operator, we have .
We note that , because every non-backtracking path starting at a given vertex is made by picking the first left-to-right edge (we have c such edges to pick from), and then alternating between picking any of the d or c edges adjacent to the current vertex, except for the edge we picked to get to it.
We write
, with
, and
. Since the graph is Ramanujan, the nontrivial eigenvalues in its spectral decomposition have their absolute value in the set
. We only care about the nontrivial eigenvalues because
; hence, in the writing of
r in the orthogonal basis made of eigenvectors, only eigenvectors with nontrivial eigenvalues appear. We decompose
, and, using the notation in Lemma 5, we obtain
and, with the result of that lemma, we have
We combine everything to obtain
□
5. Random Gadget
In this section, we prove the existence of bipartite graphs, such that every small set of left-side vertices has a unique neighbour on the right side. We drew a random biregular graph from a similar distribution as in [
31], and used techniques similar to [
32] (Thm 4.4).
Lemma 6. For every integer and d with , , , if k is an integer that satisfies the inequalitythen there is a -biregular graph with sides and , such that every set of left vertices of size k at most has a unique neighbour. We drew a random -biregular graph in the following way: we fixed L vertices on the left side and R vertices on the right side (), wrote c copies of each left-side vertex and d copies of each right-side vertex, and connected them via a uniformly random matching. That is, we picked a uniformly random permutation , and, for every , if , then added as an edge. It should be noted that we allowed multiple edges between two vertices (if there were several satisfying ).
Let G be a random bipartite graph with L vertices on the left side and R vertices on the right side drawn from said distribution. Let A be a subset of left vertices of size k. We note that, if A expands by at least , then, by a simple counting argument, A has a unique neighbour. It is therefore sufficient to find the probability that A expands by at least .
Let us fix an arbitrary ordering of the edges leaving A, and denote it . We say that is a repeat if it touches a previously covered vertex, that is, if its right endpoint is contained in the set of right endpoints of the set . We note that if A does not expand by at least , then, again, by a simple counting argument, there are at least repeats. This is because the number of repeats and the size of the set of the neighbours of A add up to the number of edges leaving A, namely .
We note that for every
i,
is a repeat if it touches one of
or less previously covered vertices. This means that
. Moreover, if we condition on the event that some of the first
edges are also repeats, then the probability that
is a repeat may only decrease, since it means that there are less “forbidden” endpoints. We conclude that, for every set of
l edges,
If
A expands too little, then there are many repeats. We can use it to bound the probability that
A has no unqiue neighbour:
Furthermore, by a union bound over the possible choices of
A,
where the last inequality follows from assuming that
, so
.
We are now ready to prove Lemma 6.
Proof of Lemma 6. Let us draw a
-biregular graph
from the distribution described above. Let
k be an integer satsifying (6). Using a union bound and the inequality in (
11), we have (where probability is taken over the choice of
G)
We see that with strictly positive probability, a random graph has no “bad” subsets of size ; hence, there exists a graph with the desired unique neighbour property. □
6. Construction
6.1. Routed Product Definition
Let us begin with a brief coding theory motivation. An error-correcting code is often given via an
parity check matrix
H, so that
. The matrix
H can be visualised as a bipartite graph, called the
parity check graph, with
n left and
m right vertices, and an edge
whenever
. A Tanner code is defined given a bipartite graph
B and a base code
[
33]. One way to view the routed product is through the point of view of codes. We consider the parity check graph
of
and define the
routed product of
B and
to be simply the parity check graph of the Tanner code
.
Here is a more detailed and combinatorial definition of the routed product without mention of codes: let be a -biregular graph and a -biregular graph.
We think of G as a big graph (in practice, an infinite family of Ramanujan graphs), and as a fixed-size graph (gadget). We assume that , and think of the edges of G as a function which maps a right-side vertex v and an index i to the ith neighbour of v in G.
We can define the
routed product graph
as the bipartite graph whose left side is
L, right side is the Cartesian product
, and the set of edges is
That is, we write
copies of each vertex in
R, and every right-side vertex
v in the big graph
G and an edge
in the small gadget
gives an edge between the
ith neighbour of
v in
G, and the
vertex of the copy of
assigned to
v in
. Otherwise put, we use
to route every edge of the big graph
G to
edges in the product graph
.
More precisely, for every , the bipartite subgraph of , whose left side is and right side is , is isomorphic to . This means that, roughly speaking, unique neighbours are inherited from the small graph to the product graph:
Lemma 7. Let , . We define as the indexed neighbours of v in S. If , as a set of vertices in the gadget , has a unique neighbour in , then is a unique neighbour of S in the product graph .
The proof is immediate while observing
Figure 1, but, for the sake of completion, it is given in
Appendix A.
6.2. Proof of Theorem 1
Let q be a prime power, an integer, and . We assume that is an integer. We construct an infinite family of -biregular graphs with the unique neighbour property under some assumptions specified below.
We denote and . By Theorem 3, there is an efficient construction of an infinite family of -biregular Ramanujan graphs . Let be a gadget: a -left-regular bipartite graph with vertices on the left side and vertices on the right side, such that every left-side set of sufficiently small size admits a unique neighbour on the right side, where “sufficiently small” means the bound given in Lemma 6. For the constructed graph to have the left side times bigger than the right side, we set .
We define as the routed product of and . For the rest of this (short) proof, let us suppress n from the notation, for convenience.
Let
. By Theorem 2, there exists
, such that, for every
of size
at most, the “average right degree”
, namely, the average of the degrees of vertices in
in the induced subgraph
, is bounded:
We show that such
S has a unique neighbour in
.
We note that , so, since , we have a vertex of “degree” at most in G, that is, the set of v’s neighbours in S is of size at most. By Lemma 7, if , as a set of left-side vertices in , has a unique neighbour j in , then our original set S has a unique neighbour in G.
The parameters now need to be chosen in a way that all left-side sets of size
at most have a unique neighbour in
. By Lemma 6, we need to have
The LHS is
and RHS is
, so, if
, then, for sufficiently large
q, the construction gives a unique neighbour expander. That is, there exists some
, such that, if
, then (
12) holds; hence, we constructed a bipartite unique neighbour expander, as promised in Theorem 1.
7. Future Work
The main pitfall of our approach is the non-constructive nature of the gadget. Theoretically, since the gadget has a constant size, this is no issue. However, exhaustive search is impractical, even for small values of
q. This is because the gadget’s size is cubic in
q, so the search space is of exponential size in
. A natural question would be whether it is possible to construct such a gadget in an efficient way, since that would lead to the whole unique neighbour expander family to be constructible in practice. For the bipartite Ramanujan family chosen in our work (the one by Ballantine et al. [
29]), we ask the following:
Question 1. For which prime power q and real number can one efficiently construct a biregular graph with left side and right side , such that every left-side set of size at most has a unique neighbour?
We note that the fixed-size graph given in [
1] (Lemma 4.3) is a good gadget (for
and the edge–vertex incidence graphs of a 44-regular Ramanujan graph family), and, indeed these graphs can be used to construct bipartite unique neighbour expanders.
Since we proved that a random gadget is, with non-negligble probability, good for our construction, it may be interesting to construct such a gadget by simply drawing random gadgets and testing whether they are good. Since drawing is simple, we are left with the task of testing. We therefore ask
Question 2. Given a bipartite graph, can one efficiently find the smallest nonempty set of left-side vertices that has no unique neighbours?
We currently know of no better way than just enumerating all left-side sets, which is exponential in the size of the graph, hence impractical. We refer to [
11] for an interesting approach to testing the expansion of random graphs.
The methods presented in this work are not limited to the -biregular Ramanujan family. We can therefore ask the question the other way around—find a gadget (by sampling or any other way)—and see whether we can efficiently construct a bipartite Ramanujan family that will make it work, i.e., that would allow us to rewrite the proof of Theorem 1. This emphasises the well-known natural question of constructing Ramanujan graphs with arbitrary degrees, specifically in the bipartite and biregular setting,
Question 3. For which integers can one efficiently construct an infinite family of -biregular Ramanujan graphs?
We note that our construction is far from “right-side unique neighbour expansion,” as the complete right side of a single gadget is a constant-sized set with no unique neighbours on the left. We wonder whether it is possible to construct a bipartite graph where all small size sets (be they contained in either side, or both) have unique neighbours.