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1 Introduction
Let be a simple connected graph (without loops and multiple edges). The distance
of two vertices
is the number of edges in a shortest path between
and
. The Wiener index
of
is the sum of these distances for all distinct pairs
. This concept was introduced by Harry Wiener, who showed that this graph parameter is closely correlated with some chemical parameters, for instance with the boiling points of alkane molecules [Citation1]. As an example, the Wiener indices of a large classes of fullerenes are determined in [Citation2]. The book [Citation3] contains many analogues and generalizations of the Wiener index, all of them have relevance in chemistry.
Let us introduce a new vector-parameter, the Wiener profile as the -dimensional vector
where
is the number of pairs of distinct vertices
with distance
. It is easy to see that the Wiener index is equal to
The contribution of a pair
of vertices (atoms in a molecule) to the Wiener index is
if their distance is
. However, if we want to have a parameter which indicates a certain chemical property then the contribution of a pair
could be different from their distance. Suppose that it is some
in case when
. Here we supposed that the contribution of the pair
to the property depends only on their distance. But the choice of the numbers
might depend on the chemical property in question. Let
be the vector of these coefficients. Then the weighed Wiener index with these coefficients is
The choice
gives back the Wiener index.
We will study the structure of the set of Wiener profiles of graphs on vertices. Suppose e.g. that we want to find the molecule containing
atoms and having a certain chemical property the most or least, then we have to maximize or minimize
under the condition that
is the Wiener profile of a graph of
vertices (
is fixed!). From the chemical point of view it makes sense only to consider connected graphs, but technically it is easier to handle the set of all graphs on
vertices. If a graph is not connected disregard the pairs in different components. Take all Wiener profiles of the graphs on at most
vertices (in other words the subgraphs of the
-vertex complete graph
.) Denote this set by
. It is a set of points with integer coordinates in the
-dimensional Euclidean space.
Let us note that profile vectors were also studied in Extremal Set Theory (see e.g. [Citation4]). There is, however a huge difference between the structures of the sets of profile vectors there and here. In Extremal Set Theory, if the coordinates of a profile vectors are decreased (remaining in the non-negative range) then the new vector is also a profile vector. This is not true here at all, as the shows. There are 4 non-isomorphic graphs on three vertices: the complete graph
, the path of two edges, a single edge and the empty graph. Their profiles in this order are
. That is,
. One can see that
is not a profile though
is.
Our main task is to give necessary and/or sufficient conditions on vectors to make them Wiener profiles. There is one trivial condition. Since is counting the number of distances
, their sum counts the total number of distances.
But there are more complex relations among the coordinates
. We will introduce them below. However, before that, we suggest a new invariant of graphs that intuitively also has significance in chemistry. In the Wiener index and in the Wiener profile the pairs of vertices with distance
are considered. The distance is defined by a shortest path between the two vertices. But there might be more paths of the same (shortest) length between the vertices. The molecule is probably more rigid if the vertices are connected with more paths. This motivation makes us to introduce the concept of the path profile. Let
be the number of paths of length
in the graph
containing
vertices
. The path profile is the vector
. The path index is then
Here we will call the attention on some more complex necessary conditions that ’s and
’s must satisfy. Suppose that
is known, fixed. One can intuitively feel that
cannot be too small. Indeed, the graph contains
paths of length
, these paths contain many paths of length
. This fact must give a lower bound on the number
. Let us use a somewhat different approach, considering the set
of edges. The
paths are its
-element subsets. Deleting the first or last edge from such a path, a path of length
is obtained. What is the minimum number of these
-element subsets? The literature knows a very closely related problem/results. Let
be a family of
-element sets. The shadow
is a family of those
-element sets which are obtained from the members of
deleting one (arbitrary) element. The Shadow Theorem [Citation5,Citation6] determines the exact minimum of
for given
and
(where
denotes the size of the set
). We will study an analogous problem here.
The Path Shadow Problem. Knowing the number of paths of length
in a graph, determine (or estimate) the minimum number of subpaths of length
(or in general of length
). There are substantial differences between this problem and the traditional Shadow Problem. Here the
-element subsets of edges are not necessarily paths of length
, in contrast to the case of the Shadow Problem. On the other hand, in the new problem we cannot delete edges arbitrarily from a path obtaining a shorter path.
The Distance Shadow Problem is formally very similar. Given , the number of pairs of distinct vertices with distance
, determine the minimum number of distances
in a graph. Our present paper is devoted to these two problems.
2 Minimization of path and distance shadows
Given a graph let
be the number of pairs of distinct vertices
with distance
. Our goal in this section is to find the minimum of
for all graphs containing
pairs with distance
where
. Denote the minimum by
. If
, the number of vertices is known then we can define the minimum
. These quantities are the minimum sizes of the distance shadow. The following inequality is obvious.
(1)
(1)
But there is an another variant of this problem. The distance is defined by a (shortest) path. In our modified problem we consider all paths of length
, not only the ones which define the distance. Let
denote the minimum number of paths of length
in a graph containing exactly
paths of length
. Similarly, let
denote the same under the condition that the number of vertices of the graph is fixed:
. The following inequality is obvious, again.
(2)
(2) These are the minimum sizes of the path shadows.
2.1 The case ![](//:0)
, paths
The results in this subsection are taken from the literature. We list them for the sake of completeness.
Here the number of distances 1 and the number of paths of length 1, that is the number of edges should be minimized in both cases. It is easier to handle the following inverse problem. The number of edges in the graph is given, determine the maximum number of distances or paths of length
in the graph, either fixing the number of vertices or not.
Let the inverse of be
. This is the maximum number of paths of length
in a graph containing
edges. Similarly, let
denote the same under the condition that not only the number of edges, but also the number of vertices is given:
and
, respectively. It is easy to determine
.
Proposition 1
Proof
The star (one “central” vertex contained in all edges) gives the above value, proving
On the other hand a path of length two consists of two edges, therefore one cannot have more of them than
. □
It is more difficult to determine . Paper [Citation7] contains an almost complete solution. In order to be able to formulate its main statement some definitions are needed. A quasi-clique consists of a clique (complete graph) and an additional vertex adjacent to a subset of the vertices of the clique. (If there is no additional vertex then the quasi-clique is simply a clique.) A quasi-star is the complement of a quasi-clique.
Theorem 1
[Citation7]
is equal to the number of paths of length two either in a quasi-star or in a quasi-clique with
edges. Moreover the extremal graph is a quasi-star if
and a quasi-clique if
.
One can see from this special case that the determination of is easier than that of
. However in the case of
the difference is not so much.
Theorem 2
[Citation8]
If holds then
with equality for the complete graph on
vertices.
Here we do not have the exact value for all , but this theorem gives a very good asymptotic estimate for
. Moreover, the best (asymptotic) construction does not depend on
. Therefore the asymptotic solution (that is sharp for infinitely many
) gives the asymptotically sharp solution for
, too.
Based on these results one can guess that the odd and even ’s behave differently.
Theorem 3
[Citation9], see also [Citation8]
If is odd then both
and
are asymptotically equal to
and the asymptotically sharp construction is a complete graph.
Theorem 4
[Citation8]
If is even then
is asymptotically equal to
, where
is a constant depending only on
.
If , then
is basically known.
Theorem 5
[Citation10]
If is large enough then
The extremal construction is a complete bipartite graph with sizes
and 2.
Very little is known on , but
is asymptotically determined.
Theorem 6
[Citation11]
is asymptotically equal to the number of paths either in a quasi-star or in a quasi-click.
2.2 The case ![](//:0)
, distances
We consider the inverse again. The inverse of is
: this the maximum number of distances
in a graph with
edges, while
is the maximum number of distances
in a graph with
vertices and
edges. Of course
(3)
(3) holds. The inequalities are usually very sharp, since, e.g. the number of distances is at most quadratic in
, while the number of paths can be much larger.
Proposition 2
Proof
The statement follows from Proposition 1, (Equation3(3)
(3) ) and the fact that the star with
rays contains this many distances 2. □
Proposition 3
Proof
If then one cannot have more than
paths of length 2, therefore this is an upper bound for the number of distances, as well. The star of
edges gives equality. On the other hand, if
then the distance between the endpoints of an edge is one, cannot be two. Hence the total number of distances two cannot be more than
. The star with
rays and
other edges gives the equality. □
Theorem 7
For fixed and large
is asymptotically equal to
Proof
Let us see that the number of finite distances in a graph with edges cannot exceed
. If the graph is connected then the number of vertices is at most
, therefore the number of distances is at most
. Suppose now that the numbers of edges in the
components are
where
. The
’s component has at most
vertices therefore at most
edges. The total number edges is at most
This is really at most
as the following lines show:
Since and
are asymptotically equal, the latter one is really an asymptotical upper bound.
Below we will construct asymptotically optimal graphs, distinguishing cases according to .
Suppose first that is even. Let
be a complete
-ary tree of depth
. This is a tree with a root of degree
, all other vertices have degrees
and 1. The latter one are the leaves. The distance of the root and a leaf is
. The number of edges is
(4)
(4) where it is supposed in the asymptotical calculation that
is fixed and
is large. Denote the vertices of distance 1 from the root by
, while the set of leaves connected to the root through
is denoted by
. It is easy to see that the distance of the vertices
and
in the tree
is exactly
. Of course
. Hence the number of pairs of vertices with distance
is at least (in fact exactly)
Using (Equation4
(4)
(4) ) we really have that the number of distances
is asymptotically
if
is fixed and
is large. This proves the statement for even
.
Suppose now that is odd. Define a graph
in the following way. It will consist of a complete graph
and
copies of the rooted tree
, one copy attached to every vertex of
. The set of leaves of the copy attached to the
th vertex is denoted by
. The number of edges of
is
(5)
(5) The distance of the vertices
and
in
is exactly
. Of course
. Hence the number of pairs of vertices with distance
is at least (in fact exactly)
Using (Equation5
(5)
(5) ) we obtain that the number of distances is
, as stated.
In the case of the construction above should be slightly modified. Here we attach a star with
edges to each vertex of the complete graph
. The endpoints of different stars have distance 3. The number of edges is
(6)
(6) The number of pairs with distance 3 is
where (Equation6
(6)
(6) ) was used.
Our constructions show the statement for an infinite sequence of values of , namely for the values of form (Equation(4)
(4)
(4) Equation(6)
(6)
(6) ) in the respective cases. This would be sufficient if we knew that
is a monotone function of
. However it is absolutely non-trivial that adding an edge to a graph increases the distances
. Here, however only the monotonicity of our construction is needed.
Suppose that is even and
Add
edges to
without forming a cycle. No distance
can be destroyed in this way in
. Therefore the number of distances
in this new graph is at least as much as in
. This proves that the number of distances
can asymptotically be
when
is asymptotically at most
.
The other cases of can be similarly settled. □
If is very close to
then there is a hope to get the exact value of
.
Conjecture 1
If is fixed,
is large enough then
.
The construction giving this value is two stars with and
edges connected with a path of
edges. One easily prove the conjecture for
2.3 The case ![](//:0)
, paths
Here we consider the inverse problem, again. Let be the largest number of paths of length
in a graph containing
paths of length
. The most important case is
. Having a good upper estimate on
(and
) we get one for
, and so on. Make our notation shorter:
.
Proposition 4
is asymptotically lower bounded by
.
Proof
The complete graph serves as a construction. The number of paths of length
is
(choosing an ordered sequence of vertices of length
, we have to divide the product by 2 because every path is counted starting from both ends). Hence
that is
. The number of paths of length
is
□
Observe that this upper bound is too weak if . It gives
while we know by Proposition 1 that
However we believe that the bound is asymptotically sharp starting from
.
Conjecture 2
Theorem 8
Conjecture 1 is true for regular graphs.
Proof
Let denote the number of vertices of the regular graph of degree
. The number of paths of length 2 with middle vertex at a given vertex is
. The total number of paths of length 2 is
(7)
(7) The number of paths of length 3 with a fixed edge in the middle is
, since we have
ways to continue the path at each end of the fixed edge. The total number of paths of length 3 is obtained by multiplying this with the number of edges. The number of edges is
. Hence the number of paths of length 3 is
(8)
(8) We know by Proposition 4 that
is an asymptotic lower bound. Here we will prove that it is also an upper bound for (Equation8
(8)
(8) ) that is for the number of paths of length 3:
(9)
(9) Use (Equation7
(7)
(7) ) on the right hand side.
Writing out the details this becomes
Divide both sides by
We have arrived to
This is true, since the degree cannot exceed the number of vertices. (Equation9
(9)
(9) ) and the theorem are proved. □
2.4 The case ![](//:0)
, distances
Let be the largest number of distances of length
in a graph containing
distances of length
. The most important case is
. Having a good upper estimate on
(and
) we get one for
, and so on. Make our notation shorter:
.
Of course and Proposition 2 gives the exact solution. The number of distances 2 is a quadratic function of the number of distances 1 (edges). We believe that this cannot happen for larger
’s. Actually, it is difficult to construct a graph in which the number of distances 3 is much larger than the number of distances 2.
Theorem 9
where
means logarithm of basis 2.
The proof will be divided into lemmas.
Let the vertices of a graph be the sequences of length
formed from the elements
. Two vertices are adjacent if the corresponding sequences are equal in all but one place where the difference of the values is 1 or
. This is actually a
“grid” containing
vertices. Let
denote the number of vertices having distance
from the origin
. The case
is trivial.
(10)
(10)
Lemma 1
.
Proof
Let denote the set of vertices of distance 2 from the origin in
. Divide
into subsets according to their first coordinates:
Then
(11)
(11)
The elements of have distance 2 in the rest, that is in
. Hence we have
(12)
(12) The elements of
have a value 1 in the first coordinate. Therefore their rest have distance 1 from the origin. Hence we have
(13)
(13) The same holds for
:
(14)
(14) Finally, if the first coordinate is
or
then the other
coordinates must be all 0. Hence we have
(15)
(15)
Eqs. (10)–(15) give
(16)
(16) Now use induction on
to prove the statement of the lemma. For
, it is trivial that there are two vertices of distance 2 from the point 0. Suppose now that the statement holds for
and prove it for
. By (Equation16
(16)
(16) ) and the inductional hypothesis we can write
Lemma 2
.
Proof
The logic of the previous proof will be used again. Let denote the set of vertices of distance 3 from the origin in
. Divide
into subsets according to their first coordinates:
Then
(17)
(17)
The elements of have distance 3 in the rest, that is in
. Hence we have
(18)
(18) The elements of
have a value 1 in the first coordinate. Therefore their rest have distance 2 from the origin. Hence we have
(19)
(19) The same holds for
:
(20)
(20) If the first coordinate of a vertex is 2 then the rest must have distance 1 from the origin. This proves
(21)
(21) and
(22)
(22) Finally, if the first coordinate is
or
then the other
coordinates must be all 0. Hence we have
(23)
(23)
Eqs. (17)–(23) give
(24)
(24) and, using the statement of Lemma 1,
(25)
(25) Now use induction on
to prove the statement of the lemma. For
, it is trivial that there are two vertices of distance 3 from the point 0. Suppose now that the statement holds for
and prove it for
. By (Equation25
(25)
(25) ) and the inductional hypothesis we can write
Proof of Theorem 9
Modify the graph at the beginning of our proof. Let the vertices of the graph
be the sequences of length
formed from the elements
and consider these integers modulo 8. Two vertices are adjacent if the corresponding sequences are equal in all but one place where the difference of the values is 1 or
(mod 8). This is actually a
“cyclic grid” containing
vertices. The 3-neighborhood of a vertex
in a graph is the subgraph spanned by the set of vertices of distance at most 3 from
. Observe that the 3-neighborhoods of the origins in
and
are isomorphic. Moreover the 3-neighborhoods of two distinct vertices in
are also isomorphic. Hence it follows that the number of vertices with distance 3 (resp. 2) from a vertex of
is the same as the number of vertices with distance 3 (resp. 2) from the origin of
. Summarizing, the number of vertices with distance 3 (resp. 2) from a vertex of
is
(resp.
). Therefore the total number of distances 2 in
By Lemma 1 this is equal to
(26)
(26) Similarly, the total number of distances 3 in
and by Lemma 2 this is equal to
(27)
(27) Here (Equation26
(26)
(26) ) and (Equation27
(27)
(27) ) lead to
(28)
(28) Eq. (Equation26
(26)
(26) ) gives us
Compare this with the left hand side of (Equation28
(28)
(28) ). One can see that
(29)
(29) The inequalities (Equation28
(28)
(28) ) and (Equation29
(29)
(29) ) prove the statement of the theorem in a stronger form, but only for some special values of
, which form an exponential sequence.
Let us prove now the weaker inequality for the intermediate values of . Suppose
(30)
(30) Add
paths of length 2 to
in such a way that they do not form any cycle neither with
nor with each other. The so obtained graph
contains
pairs with distance 2 and at least (Equation27
(27)
(27) ) pairs with distance 3.
Observe that (31)
(31) Formulas (Equation30
(30)
(30) ) and (Equation31
(31)
(31) ) imply
Combining this with (Equation29
(29)
(29) ) the desired inequality is obtained. □
Open Problem 1
Find a non-trivial upper bound on .
Notes
Peer review under responsibility of Kalasalingam University.
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