Generalized spectral characterization of signed trees
Abstract
Let be a tree with an irreducible characteristic polynomial over . Let be the discriminant of . It is proved that if (which is always an integer) is odd and square free, then every signed tree with underlying graph is determined by its generalized spectrum.
Keywords: Graph spectra; Cospectral graphs; Determined by spectrum; Rational orthogonal matrix; Signed graph
Mathematics Subject Classification: 05C50
1 Introduction
It is well known that the spectra of graphs encode a lot of combinatorial information about the given graphs. A major unsolved question in spectral graph theory is: “What kinds of graphs are determined (up to isomorphism) by their spectrum (DS for short)?”. The problem originates from chemistry and was raised in 1956 by Günthard and Primas [1], which relates Hückle’s theory in chemistry to graph spectra. The above problem is also closely related to a famous problem of Kac [12]: “Can one hear the shape of a drum?” Fisher [11] modelled the drum by a graph, and the frequency of the sound was characterized by the eigenvalues of the graph. Hence, the two problems are essentially the same.
It was commonly believed that every graph is DS until the first counterexample (a pair of cospectral but non-isomorphic trees) was found by Collatz and Sinogowitz [2] in 1957. Another famous result on cospectral graphs was given by Schwenk [16], which states that almost every tree is not DS. For more constructions of cospectral graphs, see, e.g., [10, 13, 17]. However, it turns out that showing a given graph to be DS is generally very hard and challenging. Up to now, only a few graphs with very special structures are known to be DS. We refer the reader to [7, 8] for more background and known results.
In recent years, Wang and Xu [19] and Wang [20, 21] considered a variant of the above problem. For a simple graph , they defined the generalized spectrum of as the spectrum of together with that of its complement . A graph is said to be determined by its generalized spectrum (DGS for short), if any graph having the same generalized spectrum as is necessarily isomorphic to .
Let be a graph on vertices with adjacency matrix . The walk-matrix of is defined as
where is the all-one vector. Wang [20, 21] proved the following theorem.
The problem of spectral determination of ordinary graphs naturally extends to signed graphs. This paper is a continuation along this line of research for signed graphs in the flavour of Theorem 1.1.
Let be the discriminant of a tree (see Section 4 for the definition). The main result of the paper is the following theorem.
Theorem 1.2.
Let be a tree on vertices with an irreducible characteristic polynomial over . If (which is always an integer) is odd and square free, then every signed tree with underlying graph is DGS.
As an immediately consequence of Theorem 1.2, we have
Corollary 1.3.
Let and be two cospectral and non-isomorphic trees with a common irreducible characteristic polynomial. Suppose is odd and square free. Then no two signed trees with underlying graphs and respectively are generalized cospectral.
Example 1.
Let and be two cospectral non-isomorphic trees on 14 vertices (see Fig. 1) with a common irreducible characteristic polynomial
It can be easily computed that , which is odd and square-free. Thus, according to Theorem 1.2, every signed tree with underlying graph (resp. ) are DGS. In particular, no two signed trees with underlying graphs and respectively are generalized cospectral.


Theorem 1.2 shows that whenever the underlying tree with vertices satisfies a simple arithmetic condition, then all the signed trees (including itself) whose underlying is is DGS. That is, the DGS property of all these signed trees only depends on the underlying graph . This is somewhat unexpected, since given a pair of trees and , it seems time consuming even to check whether there exist two signed trees with underlying graphs and respectively that are generalized cospectral; see Example 1.
We mention that Theorem 1.2 is the best possible in the sense that it is no longer true if has a multiple odd prime factor. Moreover, the irreducibility assumption of the characteristic polynomial of the tree is essential which cannot be removed; see Remarks 1 and 2 in Section 4.
The rest of the paper is organized as follows. In Section 2, we give some preliminary results that will be needed in the proof of Theorem 1.2. In Section 3, we give a structure theorem, which plays a key role in the paper. In Section 4, we present the proof of Theorem 1.2. Conclusions and future work are given in Section 5.
2 Preliminaries
For the convenience of the reader, we give some preliminary results that will be needed later in the paper. For more results in spectral graphs theory, we refer to [4, 6]
Let be a simple graph. A signed graph is a graph obtained from by assigning a sign or to every edge according to a mapping . We use to denote a signed graph with underlying graph and sign function (signature) . We call a signed graph a signed bipartite graph, if its underlying graph is bipartite.
Let be a subset of such that is a partition of . A switching w.r.t. (or ) is an operation that changes all the signs of edges between and , while keeps the others unchanged. Two signed graphs and are switching-equivalent if can be obtained from by a switching operation, or equivalently, there exists a diagonal matrix with all diagonal entry such that . A signed graph is balanced if every cycle contains an even number of edges with sign -1. It is well-known that a signed graph is balanced if and only it is switching equivalent to a unsigned graph.
Let be a signed graph with adjacency matrix . The characteristic polynomial of is defined as the characteristic polynomial of , i.e., , where is the identity matrix. Two signed graphs and with adjacency matrices and respectively are called generalized cospectral if
where is the all-one matrix and formally denotes the complement of (it is indeed the complement of if every edge of has been assigned a positive sign +1). A signed graph is said to be determined by the generalized spectrum (DGS for short), if any signed graph that is generalized cospectral with is isomorphic to .
A polynomial is irreducible if it cannot be factored into two polynomials with rational coefficients of lower degree. Let be an irreducible polynomial with degree and be one of its root. Then is a number field which is isomorphic to and is obtained by adding to ; see e.g. [9].
An orthogonal matrix is a square matrix such that . It is called rational if every entry of is a rational number, and regular if each row sum of is , i.e., , where is the all-one column vector. Denote by RO the set of all by regular orthogonal matrices with rational entries.
In 2006, Wang and Xu [19] initiated the study of the generalized spectral characterization of graphs. For two generalized cospectral graphs and , they obtained the following result (see also [5]), which plays a fundamental role in their method.
Theorem 2.1 ([5],[19]).
Let be a graph. Then there exists a graph such that and are generalized cospectral if and only if there exists a regular orthogonal matrix such that
(1) |
Moreover, if , then is unique and .
A graph with is called controllable (see [14]), denoted by the set of all controllable graphs on vertices. For a graph , define
Then according to Theorem 2.1, it is easy to obtain the following
Theorem 2.2 ([19]).
Let be a controllable graph. Then is DGS if and only if the set contains only permutation matrices.
The above theorems extend naturally to signed graphs. By Theorem 2.2, finding out the possible structure of all is a key to determine whether a (signed) graph is DGS.
Notations: We use (or if there is no confusion arises) to denote an -dimensional column all-one vector, and the all-one matrix. For a vector , we use to denote the Euclidean norm of .
3 A structure theorem for
The key observation of this paper is the following theorem which shows that for two generalized cospectral signed bipartite graphs with a common irreducible characteristic polynomial, the regular rational orthogonal matrix carried out the similarity of their adjacency matrices has a special structure.
Theorem 3.1.
Let and be two generalized cospectral signed bipartite graphs with a common irreducible characteristic polynomial over . Suppose that the adjacency matrices of and are given as follows, respectively:
Then there exists a regular orthogonal matrix such that , where
with and being regular rational orthogonal matrices, respectively.
Corollary 3.2.
The matrix in Theorem 3.1 is the unique rational orthogonal matrix such that .
Proof.
The irreducibility assumption of the characteristic polynomial of implies that is controllable. Then the corollary follows immediately from Theorem 2.1.
∎
To give the proof of Theorem 3.1, we need several lemmas below.
Lemma 3.3.
Let and be two generalized cospectral signed graphs with adjacency matrices and , respectively. Then .
Proof.
It can be easily computed that
Similarly, . Thus, the lemma follows.
∎
Lemma 3.4 ([15]).
, where ’s are normalized eigenvectors of associated with , for .
Lemma 3.5 ([18]).
Let be a symmetric integral matrix with an irreducible characteristic polynomial . Let be the distinct eigenvalues of . Then there exist polynomials with such that the eigenvectors of associated with can be expressed as
for .
Proof.
Let be an eigenvalue of with corresponding eigenvector . Consider the linear system of equations . By Gaussian elimination, there exist such that . Note is a number field. There exist polynomials with such that .
By the -th equation of , we have , for . Note and . By the irreducibility of , we have divides . Thus, for for , and is an eigenvector associated with .
∎
Next, we collect some simple facts about the relationships of eigenvalues/eigenvectors between the adjacency matrix of a signed bipartite graph and its bipartite-adjacency matrix .
Lemma 3.6.
Let be a signed bipartite graph with an irreducible characteristic polynomial over . Let the adjacency matrix of be . Suppose that is an eigenvector of associated with an eigenvalue . Then
-
1.
is an eigenvalue of and with corresponding eigenvectors and , respectively;
-
2.
and have the same length, i.e., ;
-
3.
(resp. ) is an eigenvector of associated with eigenvalue ;
-
4.
The characteristic polynomials of (resp. ) is irreducible over .
Proof.
Note that the characteristic polynomial of is irreducible, it follows that zero can never be an eigenvalue of , and hence must be a square matrix of order .
Let be any eigenvalue of with corresponding eigenvector . Then
(2) |
Thus, we have and , for otherwise we would have , since . It follows that
It follows from that . By we get . Note . It follows that , and hence since .
Note that
Hence, is an eigenvector of associated with eigenvalue . Since the characteristic polynomial of is irreducible, the set of all the eigenvalues of can be written as
Hence, the set of all the eigenvalues of (or ) can be write as . Since is irreducible over , is also irreducible . ∎
Now, we present the proof of Theorem 3.1.
Proof of Theorem 3.1.
Set . By Lemma 3.6, let be the eigenvalues of and with corresponding normalized eigenvectors
(3) | |||
(4) |
respectively, where are -dimensional unit vectors.
Hence, we have that for each ,
(6) |
For a fixed , we distinguish the following two cases:
Case 1. and have the same sign (resp. opposite sign), and and have the same sign (resp. opposite sign). It follows from (6) that
which implies that either i) and ; or ii) and .
Case 2. and have the same sign (resp. opposite sign), and and have the opposite sign (resp. same sign). Then
which implies that either i) and ; or ii) and .
Thus, for a fixed , we may assume that either and or and , where . Next, we show that uniformly, either and for all or and for all . This is the key technical part of the proof, which highly depends on the irreducibility assumption of .
According to Lemma 3.5, the eigenvectors of associated with eigenvalues can be expressed as , where with .
By Lemma 3.6, is an eigenvector of associated with . Note is a unit vector. It follows that and differ by at most a sign, i.e., there exists a such that , and
for some with degree less than , for . The last equality follows since the entries of the vector belong to , which is a number field. Further note that , we have for .
The above discussions apply similarly to the signed bipartite graph with adjacency matrix . Then we have for , where , with . Moreover, with with degree less than , and .
Claim 1.
If and with , then and for some , for all .
Proof.
Actually, it follows from that
(7) |
Taking squares on both sides of (7), it follows that
Note that is irreducible and . It follows that . Hence and for some , for . Similarly, we have for some , for . Next, we show that and coincide, i.e., , for all .
In fact, it follows from that
(8) |
It is easy to see that all the numerators in Eqs. (7) and (8) are non-zero. For example, if , then for by the irreducibility of . That is, for , which is ridiculous since () are eigenvectors of constituting a basis of .
Claim 2.
: If and with , then and for some , for all .
Proof.
This follows by using the same argument as Claim 1; we omit the details here. ∎
Write
If the condition of Claim 1 holds, we may replace and with and respectively, whenever for . Then we have and for . Let and Define
(13) |
Then is an orthogonal matrix and
Thus, . Next, it remains to show that is regular, i.e., , which is equivalent to and . That is, which are precisely that we have obtained before, as desired.
If the condition of Claim 2 holds, similarly, we may replace and with and respectively, whenever . Then and , for . Now let and Define
(14) |
Then is an orthogonal matrix and still holds. Moreover, it is easy to verify that . So is regular.
The proof is complete.
∎
4 Proof of Theorem 1.2
In this section, we present the proof of Theorem 1.2.
Recall that for a monic polynomial with degree , the discriminant of is defined as:
where are all the roots of .
Then it is clear that is always an integer for , and if and only if has a multiple root. Define the discriminant of a matrix , denoted by , as the discriminant of its characteristic polynomial, i.e., . The discriminant of a graph , denoted by , is defined to be the discriminant of its adjacency matrix.
Theorem 4.1 ([22]).
Let be a symmetric integral matrix. Suppose there exists a rational orthogonal matrix such that is an integral matrix. If is odd and square-free, then must be a signed permutation matrix.
However, Theorem 4.1 cannot be used directly, since the is always a perfect square for a signed bipartite graph with an equal size of bipartition, as shown by the following lemma.
Lemma 4.2.
Let be a signed bipartite graph with bipartite-adjacency matrix , where is a square matrix of order . Then .
Proof.
Let the eigenvalues of be Then the eigenvalues of are So we have
This completes the proof. ∎
Let be the constant term of the characteristic polynomial of defined as above. Then
Note that for a tree with an irreducible characteristic polynomial , the constant term of is always . Thus we have
Corollary 4.3.
Let be a tree with an irreducible characteristic polynomial. Then .
Finally, we are ready to present the proof of Theorem 1.2.
Proof of Theorem 1.2..
Let be any signed graph that is generalized cospectral with . We shall show that is isomorphic to . Note that has the same number of edges as and moreover, the assumption that is irreducible forces to be connected. Thus, is signed graph whose underlying graph is a tree (say ), and .
Note that both and are balanced as signed graphs, we have and . Let and , where and are diagonal matrices whose diagonal entries are .
By Theorem 2.1, the fact that and are generalized cospectral implies that there exists a regular rational orthogonal matrix such that
(15) |
i.e., , which is equivalent to where is a rational orthogonal matrix.
Let
By Theorem 3.1, assume without loss of generality that and Then we have . It follows that
Note that , which is odd and square-free. Thus, according to Theorem 4.1, both and are signed permutation matrices. It follows that is a signed permutation matrix. Moreover, note that is regular. Therefore, is a permutation matrix, and by Eq. (15), we conclude that is isomorphic to . The proof is complete.
∎
Remark 1.
The condition of Theorem 1.2 is tight in the sense that Theorem 1.2 is no longer true if has a multiple odd prime factor. Let the signed bipartite-adjacency matrices of two signed trees and be given as follows, respectively:
Then
which is irreducible over . However, , i.e., has a multiple factor 7 and the condition of Theorem 1.2 is not satisfied. Actually, there indeed exists a regular rational orthogonal matrix such that , where and and are given as follows respectively.
Remark 2.
Theorem 3.1 does not hold without the assumption that the characteristic polynomial of is irreducible over , even if is controllable. Let and be two signed trees with bipartite-adjacency matrices and given as follows respectively:
It is easy to verify that
which is reducible over and is controllable. Nevertheless, the unique regular rational orthogonal matrix (shown as above) such that is not the form as in Theorem 3.1.
5 Conclusions and Future Work
In this paper, we have given a simple arithmetic condition on a tree with an irreducible characteristic polynomial, under which every signed tree with underlying graph is DGS. This is a little bit surprising in contrast with Schwenk’s remarkable result stating almost every tree has a cospectral mate.
However, there are several questions remained to be answered. We end the paper by proposing the following questions:
Question 1.
How can Theorem 1.2 be generalized to signed bipartite graphs?
Question 2.
Is it true that every tree with an irreducible characteristic polynomial is DGS?
Question 3.
Is Theorem 3.1 true for controllable bipartite graphs?
For Question 1, the difficulty lies in the fact that for a signed bipartite graph , a signed graph generalized cospectral with is not necessarily bipartite. For Question 2, we know that it is not true for signed trees. For Question 3, we know that it is not true for controllable signed bipartite graphs. But generally we do not know any single counterexample to Questions 2 and 3. The above questions need further investigations in the future.
Acknowledgments
The research of the second author is supported by National Natural Science Foundation of China (Grant Nos. 11971376 and 12371357) and the third author is supported by Fundamental Research Funds for the Central Universities (Grant No. 531118010622).
The authors would like to thank Professor Huiqiu Lin from East China University of Science and Technology for useful discussions.
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