Open Access

Retracted Article: On the Kirchhoff matrix, a new Kirchhoff index and the Kirchhoff energy

  • Ayse Dilek Maden1,
  • Ahmet Sinan Cevik1Email author,
  • Ismail Naci Cangul2 and
  • Kinkar C Das3
Journal of Inequalities and Applications20132013:337

DOI: 10.1186/1029-242X-2013-337

Received: 4 March 2013

Accepted: 9 July 2013

Published: 24 July 2013

The Retraction Note to this article has been published in Journal of Inequalities and Applications 2014 2014:424

Abstract

Abstract

The main purpose of this paper is to define and investigate the Kirchhoff matrix, a new Kirchhoff index, the Kirchhoff energy and the Kirchhoff Estrada index of a graph. In addition, we establish upper and lower bounds for these new indexes and energy. In the final section, we point out a new possible application area for graphs by considering this new Kirchhoff matrix. Since graph theoretical studies (including graph parameters) consist of some fixed point techniques, they have been applied in the fields such as chemistry (in the meaning of atoms, molecules, energy etc.) and engineering (in the meaning of signal processing etc.), game theory, and physics.

MSC: 05C12, 05C50, 05C90.

Keywords

Kirchhoff matrix Kirchhoff Estrada index Kirchhoff energy lower and upper bounds

1 Introduction and preliminaries

It is well known that the resistance distance between two arbitrary vertices in an electrical network can be obtained in terms of the eigenvalues and eigenvectors of the combinatorial Laplacian matrix and the normalized Laplacian matrix associated with the network. By studying the Laplacian matrix in spectral graph theory, many properties over resistance distances have been actually proved [1, 2]. Meanwhile the resistance distance is a novel distance function on graphs which was firstly proposed by Klein and Randic [3]. As depicted and studied in [4], the term ‘resistance distance’ was used for chemical and physical interpretation.

We note that throughout this paper all graphs are assumed to be simple, that is, without loops, multiple or directed edges. We also note that a graph G with n-vertices and m-edges is called ( n , m ) -graph. Now, let us assume that G is connected and the vertices are labeled by v 1 , v 2 , , v n . By considering these vertices in the ( n , m ) -graph G, in [5], the standard distance, denoted by d i j , between two vertices v i and v j as the length of the shortest path that connects v i and v j was defined. Moreover, again by considering v i and v j , another distance (especially in molecular graphs), namely resistance distance, was investigated and denoted by r i j in such a graph G (see, for example, [1, 6]). Let J denote a square matrix of order n such that all of its elements are unity. Then, for all connected graphs with two or more vertices, the matrix L + 1 n J is non-singular with the inverse
X = x i j = ( L + 1 n J ) 1 .
After that, the resistance distance r i j was defined in terms of X as r i j = x i i + x j j 2 x i j [1]. In addition to this last distance, the matrix whose ( i , j ) -entry is r i j was called the resistance distance matrix RD = RD ( G ) which is symmetric and has a zero diagonal. As we mentioned previously, the concept of resistance distance has been studied a lot in chemical studies [2, 6]. Furthermore, by considering the resistance distance of the graph G, the sum of resistance distance of all pairs of vertices as the equation
Kf ( G ) = i < j r i j ,
(1)

which was named the ‘Kirchhoff index’ [6, 7], was introduced and investigated.

2 Kirchhoff matrix and Kirchhoff Laplacian matrix

In the following, by considering the resistance distance between any two vertices, we first define the Kirchhoff matrix Kf A ( G ) as a weighted adjacency matrix.

Let G be an ( n , m ) -graph. Then ( i , j ) -entry of the n × n matrix Kf A ( G ) is defined by
k i j = { r i j , if  v i v j , 0 , otherwise .
(2)
We recall that the Laplacian matrix of the graph G is L ( G ) = D ( G ) A ( G ) , where D ( G ) is the diagonal matrix of vertex degrees and A ( G ) is the ( 0 , 1 ) -adjacency matrix of the graph G. Using (2) for the definition of Kf A ( G ) and also using the Laplacian matrix, we can then define the Kirchhoff-Laplacian matrix Kf L ( G ) of G as
Kf L ( G ) = Kf D ( G ) Kf A ( G ) ,
where Kf D ( G ) = diag ( j = 1 n k 1 j , j = 1 n k 2 j , , j = 1 n k n j ) . For simplicity, let us label each j = 1 n k i j by k i . Then it is clear that
Kf D ( G ) = diag ( k 1 , k 2 , , k n ) .

The eigenvalues of Kf L ( G ) are denoted by μ 0 < μ 1 < < μ b such that the smallest eigenvalue is μ 0 = 0 with eigenvector j = ( 1 , 1 , , 1 ) . Since we have assumed that G is connected, while the multiplicity of μ 0 is one, multiplicities of the remaining eigenvalues can be denoted by s 0 = 1 , s 1 , , s b .

After all, we can define a new Kirchhoff index in the form
Kf + ( G ) = i < j k i j ,
(3)

where each k i j is as given in (2).

By considering Kf L ( G ) , it is actually easy to rewrite the new Kirchhoff index, defined in (3), as a new form. In fact, this shows that Kf + ( G ) is completely determined by the Kirchhoff-Laplacian spectrum. In detail, by considering this new form of Kf + ( G ) (see (4) below), the ordering among the eigenvalues μ l ( 1 l b ) of Kf L ( G ) and the equality
2 Kf + ( G ) = Tr ( Kf L ( G ) ) = l = 1 b s l μ l ,

we can obtain a new lower and upper bound (see (5)) for this new Kirchhoff index as in the following proposition.

Proposition 1 For an ( n , m ) graph G, let Spec ( Kf L ( G ) ) be the Kirchhoff-Laplacian spectrum of G, defined by
Spec ( Kf L ( G ) ) = { μ 0 1 , μ 1 s 1 , , μ b s b } .
Then a new Kirchhoff index of G is defined by
Kf + ( G ) = 1 2 l = 1 b s l μ l
(4)
and bounded by
( n 1 ) 2 μ 1 Kf + ( G ) ( n 1 ) 2 μ b .
(5)

3 On the Kirchhoff energy of a graph

It is known that there are quite wide applications based on eigenvalues of the adjacency matrix in chemistry [8, 9]. In fact one of the chemically (and also mathematically) most interesting graph-spectrum, based on quantities in the graph energy, is defined as follows.

Let G be an ( n , m ) -graph and let A ( G ) be its adjacency matrix having eigenvalues λ 1 , λ 2 , , λ n . We note that by [10] these λ’s are said to be the eigenvalues of the graph G and to form its spectrum. Then the energy E ( G ) of G is defined as the sum of the absolute values of these eigenvalues as
E = E ( G ) = i = 1 n | λ i | .

We may refer to [1113] for more details and new constructions on the graph energy. In view of evident success of the concept of graph energy, and because of the rapid decrease of open mathematical problems in its theory, energies based on the eigenvalues of other graph matrices have been introduced very widely. Among them the Laplacian energy LE ( G ) , pertaining to the Laplacian matrix, can be thought of as the first [14]. We note that the theory of energy-like graph invariants was firstly introduced by Consonni and Todeschini in [15]. Later on, Nikiforov [16] extended the definition of energy to arbitrary matrices making thus possible to conceive the incidence energy [17] based on the incidence matrix, etc.

As in other energies mentioned in the above paragraph, we can define a new energy by considering the Kirchhoff matrix given in (2) as follows.

If G is an ( n , m ) -graph, then the Kirchhoff energy of G, denoted by EKf ( G ) , is equal to
EKf ( G ) = i = 1 n | δ i | ,
(6)

where each δ i (with ordering δ 1 δ 2 δ n ) denotes the eigenvalues of the Kirchhoff matrix Kf A ( G ) . Basically, these eigenvalues are said to be the K-eigenvalues of G.

3.1 Bounds for the Kirchhoff energy

In this subsection we mainly present upper and lower bounds over the Kirchhoff energy defined in (6).

The first result is the following.

Theorem 1 Let G be a graph with n 2 vertices. Then
EKf ( G ) n κ ,

where κ is the sum of the squares of entries of the Kirchhoff matrix Kf A ( G ) .

Proof We have that
EKf ( G ) = i = 1 n | δ i | and i = 1 n δ i 2 = κ = 2 1 i < j n ( k i j ) 2 .
By the Cauchy-Schwartz inequality, we get
EKf ( G ) | δ 1 | + | δ n | + ( n 2 ) i = 2 n 1 δ i 2 = | δ 1 | + | δ n | + ( n 2 ) ( κ δ 1 2 δ n 2 ) .
Consider the function
f ( x , y ) = x + y + ( n 2 ) ( κ x 2 y 2 ) , where  x > 0 , y > 0 .
Now our aim is to find the maximum value of f ( x , y ) . To do that, we need to calculate the derivatives
f x = 1 x n 2 κ x 2 y 2 , f y = 1 y n 2 κ x 2 y 2 , f x x = ( κ y 2 ) n 2 ( κ x 2 y 2 ) 3 / 2 , f x y = f y x = x y n 2 ( κ x 2 y 2 ) 3 / 2 and f y y = ( κ x 2 ) n 2 ( κ x 2 y 2 ) 3 / 2 .
By a simple calculation, it is clear that the equality f x = f y = 0 implies x = y = κ n and then, for this equality between x and y, it is also true that
f x x < 0 and f x x f y y f x y 2 = ( n 2 ) ( κ x 2 y 2 ) ( κ x 2 y 2 ) 3 > 0 .
The calculations above actually conclude that f ( x , y ) has a maximum value at x = y = κ n and the required maximum value of this function is
2 κ n + ( n 2 ) ( κ 2 κ n ) = n κ .

Hence the result. □

The following lemma is needed for our other results that are given in this paper.

Lemma 1 Let G be a connected ( n , m ) -graph and let δ 1 , δ 2 , , δ n be the K-eigenvalues of G. Then
i = 1 n δ i = 0
and
i = 1 n δ i 2 = 2 1 i < j n ( k i j ) 2 .
(7)
Proof We clearly have i = 1 n δ i = Tr [ Kf A ( G ) ] = i = 1 n k i i = 0 . Moreover, for i = 1 , 2 , , n , the ( i , i ) -entry of [ Kf A ( G ) ] 2 is equal to j = 1 n k i j k j i = j = 1 n ( k i j ) 2 . Hence, we obtain
i = 1 n δ i 2 = Tr [ Kf A ( G ) ] 2 = i = 1 n j = 1 n ( k i j ) 2 = 2 1 i < j n ( k i j ) 2 ,

as required. □

Theorem 2 If G is a connected ( n , m ) -graph, then
2 1 i < j n ( k i j ) 2 EKf ( G ) 2 n 1 i < j n ( k i j ) 2 .
Proof In the Cauchy-Schwartz inequality ( i = 1 n a i b i ) 2 ( i = 1 n a i 2 ) ( i = 1 n b i 2 ) , if we choose a i = 1 and b i = | δ i | , then we get
( i = 1 n | δ i | ) 2 n i = 1 n δ i 2 ,
from which
EKf ( G ) 2 2 n 1 i < j n ( k i j ) 2 .

Therefore this gives the upper bound for EKf ( G ) .

Now, for the lower bound of EKf ( G ) , we can easily obtain the inequality
EKf ( G ) 2 = ( i = 1 n | δ i | ) 2 i = 1 n | δ i | 2 = 2 1 i < j n ( k i j ) 2 ,

which gives directly the required lower bound.

We should note that there is the second way to prove the upper bound that can be presented as follows.

Let us consider the sum
M = i = 1 n j = 1 n ( | δ i | | δ j | ) 2 .
By a direct calculation, we obtain
M = 2 n i = 1 n | δ i | 2 2 ( i = 1 n | δ i | j = 1 n | δ j | ) .
It follows from (7) and the definition of EKf ( G ) that
M = 4 n 1 i < j n ( k i j ) 2 2 EKf ( G ) 2 .

Here, since M 0 , we have EKf ( G ) 2 n 1 i < j n ( k i j ) 2 .

Hence the result. □

In the following, we present a new lower bound which is better than the lower bound given in Theorem 2.

Theorem 3 Let G be a connected ( n , m ) -graph and let be the absolute value of the determinant of the Kirchhoff matrix Kf A ( G ) . Then
2 1 i < j n ( k i j ) 2 + n ( n 1 ) 2 / n EKf ( G ) .

Proof In the light of Theorem 2, if we show the validity of the lower bound, then this finishes the proof.

By the definition of Kirchhoff energy given in (6) and by the equality in (7), we have
[ EKf ( G ) ] 2 = ( i = 1 n | δ i | ) 2 = i = 1 n | δ i | 2 + 2 1 i < j n | δ i | | δ j | = 2 1 i < j n ( k i j ) 2 + 2 1 i < j n | δ i | | δ j | = 2 1 i < j n ( k i j ) 2 + i j | δ i | | δ j | .
(8)
Since, for nonnegative values, the arithmetic mean is not smaller than the geometric mean, we have
1 n ( n 1 ) i j | δ i | | δ j | ( i j | δ i | | δ j | ) 1 / n ( n 1 ) = ( i = 1 n | δ i | 2 ( n 1 ) ) 1 / n ( n 1 ) = i = 1 n | δ i | 2 / n = 2 / n .
(9)

After that, by combining Equations (8) and (9), we obtain the required lower bound. □

Theorem 4 If G is a connected ( n , m ) -graph, then
EKf ( G ) 2 n 1 i < j n ( k i j ) 2 + ( n 1 ) [ 2 1 i < j n ( k i j ) 2 ( 2 n 1 i < j n ( k i j ) 2 ) 2 ] .
(10)

Proof We apply the standard procedure (see, for instance, [18, 19]) to obtain such upper bounds.

By applying the Cauchy-Schwartz inequality to the two ( n 1 ) vectors ( 1 , 1 , , 1 ) and ( | δ 2 | , | δ 2 | , , | δ n | ) , where each δ i ( 2 i n ) is a K-eigenvalue, we have
( i = 2 n | δ i | ) 2 ( n 1 ) ( i = 2 n | δ i | 2 ) , ( EKf ( G ) δ 1 ) 2 ( n 1 ) ( 2 1 i < j n ( k i j ) 2 δ 1 2 ) , EKf ( G ) δ 1 + ( n 1 ) ( 2 1 i < j n ( k i j ) 2 δ 1 2 ) .
Now, let us consider the function
f ( x ) = x + ( n 1 ) ( 2 1 i < j n ( k i j ) 2 x 2 ) .
In fact, by keeping in mind δ 1 1 , we set δ 1 = x . Using
i = 1 n δ i 2 = 2 1 i < j n ( k i j ) 2 ,
we get that
x 2 = δ 1 2 2 1 i < j n ( k i j ) 2 .
In other words, x 2 1 i < j n ( k i j ) 2 . Meanwhile, f ( x ) = 0 implies that
x = 2 n 1 i < j n ( k i j ) 2 .
Therefore f is a decreasing function in the interval
2 n 1 i < j n ( k i j ) 2 x 2 1 i < j n ( k i j ) 2
and
2 n 1 i < j n ( k i j ) 2 2 n 1 i < j n ( k i j ) 2 δ 1 .
Hence,
f ( δ 1 ) f ( 2 n 1 i < j n ( k i j ) 2 ) ,

and so the inequality in (10) holds. □

4 Kirchhoff Estrada index of graphs

As a new direction for studying indexes and their bounds, we introduce Kirchhoff Estrada index and then investigate its bounds. Moreover, we obtain upper bounds for this new index involving the Kirchhoff energy of graphs. In order to do that, we divide this section into two cases.

We first recall that the Estrada index of a graph G is defined by
EE ( G ) = i = 1 n e λ i ,
where λ 1 λ 2 λ n are the eigenvalues of the adjacency matrix A ( G ) of G (see [11, 2026]). Denoting by M k = M k ( G ) the k th moment of the graph G, we get
M k = M k ( G ) = i = 1 n ( λ i ) k ,
and recalling the power-series expansion of e x , we have
EE ( G ) = k = 0 M k k ! .

By [23], it is well known that M k ( G ) is equal to the number of closed walks of length k of the graph G. In fact the Estrada index of graphs has an important role in chemistry and physics, and there exists a vast literature that studied the Estrada index. In addition to Estrada’s papers depicted above, we may also refer the reader to [27, 28] for more detailed information such as lower and upper bounds for EE ( G ) in terms of the number of vertices and edges, and some inequalities between EE ( G ) and E ( G ) .

4.1 Bounds for the Kirchhoff Estrada index

For an ( n , m ) -graph G, the definition of the Kirchhoff Estrada index KEE ( G ) can be given as
KEE ( G ) = i = 1 n e δ i ,
(11)

where δ 1 δ 2 δ n are the K-eigenvalues of G.

Let L k = L k ( G ) = i = 1 n ( δ i ) k . Then, similar to the M k case, the equality in (11) can be rewritten as
KEE ( G ) = k = 0 L k k ! .

Thus the main result of the subsection is the following.

Theorem 5 Let G be a connected ( n , m ) -graph. Then the Kirchhoff Estrada index is bounded as
n 2 + 4 1 i < j n ( k i j ) 2 KEE ( G ) n 1 + e 2 1 i < j n ( k i j ) 2 .
(12)

Equality holds on both sides if and only if G K 1 .

Proof Lower bound. Directly from Equation (11), we get
KEE 2 ( G ) = i = 1 n e 2 δ i + 2 1 i < j n e δ i δ j .
By the arithmetic-geometric mean inequality (AGMI), we also get
2 1 i < j n e δ i δ j n ( n 1 ) ( 1 i < j n e δ i δ j ) 2 n ( n 1 ) = n ( n 1 ) [ ( i = 1 n e δ i ) n 1 ] 2 n ( n 1 ) = n ( n 1 ) ( e L 1 ) 2 n = n ( n 1 ) .
By means of power-series expansion and L 0 = n , L 1 = 0 , L 2 = 8 1 i < j n ( k i j ) 2 , we clearly obtain
i = 1 n e 2 δ i = i = 1 n k 0 ( 2 δ i ) k k ! = n + 4 1 i < j n ( k i j ) 2 + i = 1 n k 3 ( 2 δ i ) k k ! .
Since we require a lower bound to be as good as possible, it looks reasonable to replace k 3 ( 2 δ i ) k k ! by 4 k 3 ( δ i ) k k ! . Now, let us use a multiplier t [ 0 , 4 ] instead of 4 = 2 2 . We then arrive at
i = 1 n e 2 δ i n + 4 1 i < j n ( k i j ) 2 + t i = 1 n k 3 ( δ i ) k k ! = n + 4 1 i < j n ( k i j ) 2 t n t 1 i < j n ( k i j ) 2 + t i = 1 n k 0 ( δ i ) k k ! = n ( 1 t ) + ( 4 t ) 1 i < j n ( k i j ) 2 + t [ KEE ( G ) ] .
Now, for n 2 and m 1 , it is easy to see that the function
f ( x ) = x 2 + ( n x 2 ) 2 + ( 4 x ) 1 i < j n ( k i j ) 2

monotonically increases in the interval [ 0 , 4 ] . As a result, the best lower bound for KEE ( G ) is attained for t = 0 . This gives us the first part of the theorem.

Upper bound. By considering the definition and equality of KEE ( G ) , we clearly have
KEE ( G ) = n + i = 1 n k 1 ( δ i ) k k ! = n + i = 1 n k 1 | δ i | k k ! = n + k 1 1 k ! i = 1 n ( δ i 2 ) k 2 ,
and then
n + k 1 1 k ! i = 1 n ( δ i 2 ) k 2 n + k 1 1 k ! [ i = 1 n ( δ i 2 ) ] k 2 = n + k 1 1 k ! [ 2 1 i < j n ( k i j ) 2 ] k 2 = n 1 + k 0 ( 2 1 i < j n ( k i j ) 2 ) k k ! = n 1 + e 2 1 i < j n ( k i j ) 2 .

Hence, we get the right-hand side of the inequality given in (12).

In addition to the above progress, it is clear that the equality in (12) holds if and only if the graph G has all zero Kirchhoff eigenvalues. Since G is a connected graph, this only happens when G K 1 .

Hence the result. □

In [29], by considering the maximum eigenvalue, Zhou et al. presented a lower bound for the reciprocal distance matrix in terms of the sum of the i th row of it. By the same idea, one can also give a lower bound for the maximum eigenvalue δ 1 ( G ) in terms of the sum of the i th row of the Kirchhoff matrix Kf A ( G ) and for the number of vertices n. We should note that the proof of the following lemma can be done quite similarly as the proof of the related result in [29]. (At this point we recall that for simplicity, each j = 1 n k i j was labeled by k i in Section 2.)

Lemma 2 Let G be a connected graph with n 2 vertices. Then
i = 1 n k i 2 n δ 1 ( G ) ,

where k i is the sum of the ith row of Kf A ( G ) . Here, the equality holds if and only if k 1 = k 2 = = k n .

Therefore the lower bound on the Kirchhoff Estrada index of the graph G (which was one of our focusing points) can be given as the following theorem.

Theorem 6 Let G be a connected ( n , m ) -graph with n 2 . Then we have
KEE ( G ) e i = 1 n k i 2 n + n 1 e 1 n 1 i = 1 n k i 2 n .
(13)

In (13) the equality holds if and only if G = K n .

Proof As a special case of the theory, if we assume that G is a null graph N n , then for each 1 i n , we get k i = 0 and δ 1 = δ 2 = = δ n = 0 . Thus KEE ( G ) = n and equality holds in Equation (13). In the reverse part, if KEE ( G ) = n , then by AGMI, one can easily see that δ 1 = δ 2 = = δ n = 0 and hence G = N n .

As a general case, let us suppose that G N n . Therefore δ 1 > 0 . We then have
KEE ( G ) = e δ 1 + e δ 2 + + e δ n e δ 1 + ( n 1 ) ( i = 2 n e δ i ) 1 n 1 by  AGMI
(14)
e δ 1 + ( n 1 ) ( e δ 1 ) 1 n 1 since  i = 1 n δ i = 0 .
(15)
Now, by considering the function f ( x ) = e x + n 1 e x n 1 with its derivative f ( x ) = e x e x n 1 , where x > 0 , we easily conclude that f is an increasing function for x > 0 . Hence, from (14) and by Lemma 2, we get
KEE ( G ) e i = 1 n k i 2 n + n 1 e 1 n 1 i = 1 n k i 2 n .
(16)

This completes the proof of the inequality part of (13).

Now suppose that equality holds in (13). This implies that equalities also hold throughout (14)-(16). From the equality of (14) and by AGMI, we obtain δ 2 = δ 3 = = δ n . Since δ 1 > 0 and i = 1 n δ i = 0 , we must have δ 2 < 0 . Thus G is a connected graph. Moreover, from the equality of (16), we have δ 1 = k 1 = k 2 = = k n . Since δ 2 = δ 3 = = δ n and δ 1 = k i , by Lemma 2, G is a complete graph K n .

The converse part is clear, i.e., the equality holds in (13) for the complete graph K n .

Hence the result. □

4.2 An upper bound for the Kirchhoff Estrada index involving the Kirchhoff energy

Here, for a connected graph G, the main aim is to show that there exist two upper bounds for the Kirchhoff Estrada index KEE ( G ) with respect to the Kirchhoff energy EKf ( G ) .

Theorem 7 Let G be as above. Then
KEE ( G ) EKf ( G ) n 1 2 1 i < j n ( k i j ) 2 + e 2 1 i < j n ( k i j ) 2
(17)
and
KEE ( G ) n 1 + e EKf ( G ) .
(18)

Equality holds in (17) or (18) if and only if G K 1 .

Proof By considering the proof of Theorem 5, we have
KEE ( G ) = n + i = 1 n k 1 ( δ i ) k k ! n + i = 1 n k 1 | δ i | k k ! .
Moreover, by considering the Kirchhoff energy defined in (6), we get
KEE ( G ) n + EKf ( G ) + i = 1 n k 2 | δ i | k k !
which leads to (as in Theorem 5)
KEE ( G ) EKf ( G ) n + i = 1 n k 2 | δ i | k k ! n 1 2 1 i < j n ( k i j ) 2 + e 2 1 i < j n ( k i j ) 2 .

Hence we obtain the inequality in (17).

Another approximation to obtain an upper bound related to the relationship between KEE ( G ) and EK ( G ) can be presented as follows
KEE ( G ) n + i = 1 n k 1 | δ i | k k ! n + k 1 1 k ! ( i = 1 n | δ i | k ) = n + k 1 [ EKf ( G ) ] k k ! = n 1 + k 0 [ EKf ( G ) ] k k ! ,
which implies
KEE ( G ) n 1 + e EKf ( G ) ,

as claimed in (18). By a similar idea as in the previous results, the equality holds in (17) and (18) if and only if G K 1 . □

5 Final remark

As it has been mentioned in some parts of the previous sections, it is well known that some special type of matrices, indexes and energies obtained from graphs play an important role in applications, especially, in computer science, optimization and chemistry. This section is devoted to pointing out a possible new application area in spectral graph theory by considering the Kirchhoff matrix defined in this paper. Although the problem mentioned in the following paragraphs would seem easy for some of the researchers, we cannot prove it at the moment and believe that it would be kept as a future project.

In [[30], p.537], the authors defined the Kirchhoff matrix over a loopless connected digraph, say D. In fact, by using the same notation as in this reference, we can define it as a matrix K : = M x obtained from the incidence matrix M of D by deleting the row m ( x ) . After that, by an algebraic approximation over digraphs, it was depicted that K is a basis of the row space of M (such that each element in the basis set was called tension). According to Sections 7, 10 and 20 in [30], since there is a direct relationship between cycles and bonds in graphs and digraphs, and since tensions in a graph (or a digraph) are the linear combination of the tensions associated with their bonds, the authors produced the relationship between the Kirchhoff matrix over the digraph D and electrical networks (in Section 20).

In Equation (2) of this paper, we have defined the new Kirchhoff matrix in spectral graph theory and, as far as we know, there is no such study about it in the literature. Therefore, by considering the facts and results given in the previous paragraph, one can try to investigate a similar approximation to the relationship between this new Kirchhoff matrix over ( n , m ) -graph G and electrical networks.

Notes

Declarations

Acknowledgements

This work was presented in The International Conference on the Theory, Methods and Applications of Nonlinear Equations.

Authors’ Affiliations

(1)
Department of Mathematics, Faculty of Science, Selçuk University
(2)
Department of Mathematics, Faculty of Arts and Science, Uludag University
(3)
Department of Mathematics, Sungkyunkwan University

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