Generalized bilinear forms graphs and MDR codes
Li-Ping Huang

TL;DR
This paper studies the properties of generalized bilinear forms graphs over residue class rings, revealing their structure, maximum cliques, and their connection to maximum rank distance (MRD) and maximum distance separable (MDS) codes.
Contribution
It characterizes the graph-theoretic properties of $ ext{Gamma}_d$ over $ ext{Z}_{p^s}$ and links largest independent sets to optimal error-correcting codes.
Findings
$ ext{Gamma}_d$ is connected and vertex transitive.
Largest independent sets are MRD and MDS codes.
Largest independent sets can be linear codes.
Abstract
We investigate the generalized bilinear forms graph over a residue class ring . We show that is a connected vertex transitive graph, and completely determine its independence number, clique number, chromatic number and maximum cliques. We also prove that cores of both and its complement are maximum cliques. The graph is useful for error-correcting codes. We show that every largest independent set of is both an MRD code over and a usual MDS code. Moreover, there is a largest independent set of to be a linear code over .
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Taxonomy
TopicsCoding theory and cryptography · Cooperative Communication and Network Coding · Error Correcting Code Techniques
Generalized bilinear forms graphs and MDR codes
over residue class rings***Project 11371072 supported by National Natural Science Foundation of China.
Li-Ping Huang†††E-mail address: [email protected] (L. Huang)
School of Math. and Statis., Changsha University of Science and Technology, Changsha, 410004, China
Abstract
We investigate the generalized bilinear forms graph over a residue class ring . We show that is a connected vertex transitive graph, and completely determine its independence number, clique number, chromatic number and maximum cliques. We also prove that cores of both and its complement are maximum cliques. The graph is useful for error-correcting codes. We show that every largest independent set of is both an MRD code over and a usual MDS code. Moreover, there is a largest independent set of to be a linear code over .
Keywords: bilinear forms graph, residue class ring, independence number, core, maximum clique, MDR code
2010 AMS Classification: 05C25, 15B33, 05C30, 94B60, 94B65
1 Introduction
Throughout, let be a commutative local ring and the set of all units of . For a subset of , let be the set of all matrices over , and let . Let be the set of invertible matrices over . Let denote the transpose matrix of a matrix . Denote by ( for short) the identity matrix, and a block diagonal matrix where is an matrix. The cardinality of a set is denote by .
For , by Cohn’s definition [6], the inner rank of , denoted by , is the least positive integer such that
[TABLE]
Let . For , it is clear that and if and only if . When is a field, we have , where is the usual rank of matrix over a field. For matrices over , we have (cf. [6, 5, Section 5.4]): where and are invertible matrices over ; and .
For , the rank distance between and is defined by
[TABLE]
We have that , and , for all matrices of appropriate sizes over .
Let denote the residue class ring of integers modulo , where is a prime and is a positive integer. The is a Galois ring, a commutative local ring, a finite principal ideal ring (cf. [20, 27]). The principal ideal is the unique maximal ideal of , and denoted by . The is also the Jacbson radical of . When , is a finite field with elements. We have (cf. [20, 27]) that
[TABLE]
Let be the finite field with elements (where is a power of a prime). All graphs are simple [14] and finite in this paper. Let denote the vertex set of a graph . For , we write if vertices and are adjacent. Denote by the automorphism group of a graph .
The generalized bilinear forms graph over , denoted by , has the vertex set where , and two distinct vertices and are adjacent if where is fixed with . When , is the usual bilinear forms graph over . The bilinear forms graph plays an important role in combinatorics and coding theory, and it has been extensively studied (cf. [3, 8, 12, 16, 26, 28]).
Recently, the bilinear forms graph over is studied by [17]. However, the generalized bilinear forms graph over remains to be further studied. As a natural extension of the generalized bilinear forms graph over , we define the generalized bilinear forms graph over as follows. The generalized bilinear forms graph (bilinear forms graph for short) over , denoted by ( for short), has the vertex set where , and two distinct vertices and are adjacent if , where is fixed with .
MRD codes and codes over are active research topics in the coding theory (cf. [8, 11, 24, 7, 15, 22]) and [9, 10, 19]). The generalized bilinear forms graph has good application to the error-correcting codes over . In fact, we will show that every largest independent set of is both an MRD code over and a usual MDS code [25]. Moreover, there is a largest independent set of such that it is a linear code over .
The paper is organized as follows. In Section 2, we recall some properties of matrices over . In Section 3, we show that is a connected vertex transitive graph, and determine the independence number, the clique number and the chromatic number of . We also show that every largest independent set of is both an MRD code over and a usual MDS code, and there is a largest independent set of to be a linear code over . In Section 4, We will determine the algebraic structures of maximum cliques of , and show that cores of both and its complement are maximum cliques.
2 Matrices over
In this section, we recall some basic properties of matrices over .
Lemma 2.1
(see [1, Proposition 6.2.2] or [20, p.328])* Every non-zero element in can be written as where is a unit and . Moreover, the integer is unique and is unique modulo the ideal of .*
Let . For two distinct elements , we always have . Without loss of generality, we may assume that in our discussion.
Lemma 2.2
(cf. [20, p.328])* Every non-zero element in can be written uniquely as*
[TABLE]
where , .
By Lemma 2.2, every matrix can be written uniquely as
[TABLE]
where , .
Note that every matrix in can be seen as a matrix in . We define the natural surjection
[TABLE]
by for all of the form (2.1). Clearly, if . For and , We have
[TABLE]
[TABLE]
[TABLE]
By Lemma 2.1, it is easy to prove the following result.
Lemma 2.3
(cf. [21, Chap. II] or [20, p.327])* Let where , and let be a non-zero matrix. Then there are and such that*
[TABLE]
where . Moreover, the parameters are uniquely determined by . In (2.6), or may be absent.
Let , and let be the ideal in generated by all minors of , . Let denote the annihilator of . The McCoy rank of , denoted by , is the following integer:
[TABLE]
We have that ; where and are invertible matrices of the appropriate sizes; and if and only if (cf. [2]).
Lemma 2.4
(see [17, Lemmas 2.4 and 2.7])* Let () be of the form (2.6). Then is the inner rank of , and is the McCoy rank of .*
Let . By Lemma 2.4, , and if and only if . By Lemmas 2.3 and 2.4, it is easy to see that (cf. [17])
[TABLE]
For and , if , we call that has a right inverse and is a right inverse of . Similarly, if , than has a left inverse and is a left inverse of . Note that if and , then . By Lemma 2.3, we have the following lemmas.
Lemma 2.5
(cf. [17])* Let where and . Then for all . Moreover, has a right inverse if and only if .*
Lemma 2.6
(see. [17, Lemma 4.2])* If where , then .*
Lemma 2.7
(see [17, Lemma 4.3])* If where , then both and can be viewed as matrices in with the same inner rank.*
3 Independence number of and MDR codes
3.1 Independence number and chromatic number of
Recall that an independent set of a graph is a subset of vertices such that no two vertices are adjacent. A largest independent set of is an independent set of maximum cardinality. The independence number of , denoted by , is the number of vertices in a largest independent set of .
An -colouring of a graph is a homomorphism from to the complete graph . The chromatic number of , denoted by , is the least value for which can be -coloured.
A clique of a graph is a complete subgraph of . A clique is maximal if there is no clique of which properly contains as a subset. A maximum clique of is a clique of which has maximum cardinality. The clique number of , denoted by , is the number of vertices in a maximum clique. For convenience, we regard that a maximal clique and its vertex set are the same.
Theorem 3.1
Every generalized bilinear forms graph is a connected vertex transitive graph.
*Proof. *Let . For any vertex of , since the map is an automorphism of , is vertex-transitive.
Let with . By Lemma 2.3, there are and such that , where . If , then . Now we assume that . Put , , . Then and , . It follows that is connected.
For a graph , we have (see [4, Theorem 6.10, Corollary 6.2])
[TABLE]
From [23, Lemma 2.7.2] we have
[TABLE]
Now, we recall the coding theory on a finite field (cf. [7, 12, 15, 22]). Without loss of generality we assume that are integers. An rank distance code over is a subset of with for distinct . For an rank distance code over , we have , and the bound is called the Singleton bound for . If a rank distance code over satisfies , then is called an maximum rank distance code (MRD code in short) over . A linear code of length over is a subspace of .
MRD codes (over ) can be used to correct errors and erasures in network. In 1978, Delsarte [8] (and independently Gabidulin in 1985 [11], Roth in 1991 [24]) proved the following important result:
Lemma 3.2
There are linear MRD codes over for all choices of .
These linear MRD codes in Lemma 3.2 are also called Gabidulin codes. Until a few years ago, the only known MRD codes were Gabidulin codes. Recently, the research of MRD codes is active, and we know about other some MRD codes over (cf. [7, 15, 22]).
Clearly, every independent set of is an rank distance code over and vice versa. In other words, an MRD code over is a largest independent set of . Thus, Lemma 3.2 implies that
[TABLE]
The formula (3.3) was also showed by [12].
By (3.2) and (3.3), we get . On the other hand, \small\mathcal{M}:=\left(\begin{array}[]{c}\mathbb{F}_{q}^{(d-1)\times n}\\ 0\\ \end{array}\right) is a clique and . Thus we obtain
[TABLE]
Theorem 3.3
When , the independence number and the clique number of are
[TABLE]
and
[TABLE]
*Proof. *We prove (3.5) and (3.6) by induction on . When , (3.5) and (3.6) hold by (3.3) and (3.4). Suppose that and
[TABLE]
Write . Let be a largest independent set of , where , . Since each can be seen as an element in , is also an independent set of .
Put . Let be a largest independent set of . Note that every matrix over can be seen as a matrix over . For any two distinct matrices , we have . From Lemma 2.7 we get . Thus is an independent set of with . Let
[TABLE]
Then is an independent set of and , . Let
[TABLE]
Since for , Lemma 2.5 implies that is an independent set of with . Therefore,
[TABLE]
[TABLE]
On the other hand, it is easy to see that \small\mathcal{M}:=\left(\begin{array}[]{c}\mathbb{Z}_{p^{s}}^{(d-1)\times n}\\ 0\\ \end{array}\right) is a clique of and . Thus we obtain
[TABLE]
Using (3.2) and (3.10), we get
[TABLE]
It follows from (3.9) that (3.5) holds.
Let be a finite group and let be a subset of that is closed under taking inverses and does not contain the identity. The Cayley graph is the graph with vertex set and two vertices and are adjacent if . A Cayley graph is normal if for all . It is easy to see that is a normal Cayley graph on the matrix additive group of and the inverse closed subset is the set of all matrices of inner rank .
Lemma 3.4
(Godsil [13, Corollary 6.1.3], cf. [23, Therem 3.3.1])* Let be a normal Cayley graph. If , then .*
By Theorem 3.3 and Lemma 3.4, the chromatic number of is
[TABLE]
3.2 MDR codes over
We recall the usual definition of MDS code in coding theory (cf. [25]). An code over a finite alphabet is a nonempty subset of size of . For codewords , , the Hamming distance between and is . An code with minimum distance is called an * code*. For any code over an alphabet of size , we have the Singleton bound
[TABLE]
An code over an alphabet of size is called a maximum distance separable code (MDS code in short) if it attains the Singleton bound, i.e. .
A code (over ) of length is a subset of (or ). If the code is a submodule (i.e. vector space) over we say that it is a linear code over . Suppose that are integers. As a natural extension of rank distance code over , we define the rank distance code over as follows. An rank distance code over is a subset of with for distinct .
Every independent set of is an rank distance code over and vice versa. Thus, for an rank distance code over , we have by (3.5). If a rank distance code over satisfies , then is called an maximum rank distance code (MRD code in short) over . In other words, an MRD code over is a largest independent set of .
Theorem 3.5
If is a largest independent set of , then is both an MRD code over and an MDS code with . Moreover, there is a largest independent set of such that is a linear code over .
*Proof. *Step 1. Without loss of generality we assume that are integers. By (3.5), it is clear that every largest independent set of is an MRD code over . We prove that every largest independent set of is a usual MDS code as follows.
By Lemma 2.2 and (2.1), the row vector space is isomorphic to (as an -dimensional vector space over ) (cf. [27, Chapter 14]). Thus, it is easy to see that (as a vector space over ) is isomorphic to the column vector space (as a vector space over ). Let be a largest independent set of . From (3.5) we have . For two distinct vertices , since , it is clear that has at least non-zero rows. Hence the Hamming distance between and (as vectors in ) is at least . Thus, can be seen as an code over an alphabet of size , where . Since , is a usual MDS code.
Step 2. We assert that there is a largest independent set of such that it is a linear codes over . By Lemma 3.2, we may assume with no loss of generality that . Using Lemma 3.2, there is a linear MRD code over . Clearly, is a vector space over and a largest independent set of .
When , recalling (3.8), is a largest independent set of . Since contains [math], we have . By Lemma 2.2 and (2.1), one can prove that is a vector space over . For (), assume that there is a largest independent set of , such that and is a vector space over . Then by (3.8), is a largest independent set of . Since contains [math], . Applying Lemma 2.2 and (2.1), it is easy to prove that is a vector space over . By the induction on , for any , there exists a largest independent set of , such that is a vector space over . By Step 1, is a linear code over .
By Theorem 3.5, there are linear MRD codes over for all choices of . Applying (3.8), we can Construct many MRD codes over .
4 Maximum cliques and Core of
4.1 Maximum cliques of
Note that the algebraic structures of maximum cliques of have many applications. For example, a maximum clique of is a largest independent set of the complement of . We will determine the algebraic structures of maximum cliques of .
Let be an -dimensional vector subspace of . Then has a matrix representation \scriptsize\left(\begin{array}[]{c}\alpha_{1}\\ \vdots\\ \alpha_{m}\\ \end{array}\right)\in\mathbb{F}_{q}^{m\times n}. For simpleness, the matrix representation of a subspace is also denoted by . If is a matrix representation of a subspace of , then is also a matrix representation of where . It follows that the matrix representation is not unique. However, a subspace of has a unique matrix representation which is the row-reduced echelon form , where is a permutation matrix.
A geometric description of the bilinear forms graph is the adjacency graph of the attenuated space. Let and let
[TABLE]
be a fixed -dimension subspace of . Write
[TABLE]
The incidence structure is called an attenuated space. Its adjacency graph is the graph with as its vertex set, and two vertices being adjacent if their intersection is in . For any , by we have , where is uniquely determined by . Let
[TABLE]
Then is a graph isomorphism from the adjacency graph of to . Moreover, if and only if for all (cf. [18], [3, §9.5A]).
Lemma 4.1
(see [26, Theorem 3(2)])* Suppose that and is the adjacency graph of an attenuated space . Let be a collection of elements of the with the property that for all in . Then , and equality holds if and only if either (a) consists of all elements of which contain a fixed -dimensional subspace with , or (b) and is the set of all elements of contained in a fixed -dimensional subspace with .*
Lemma 4.2
In (where ), every maximum clique containing [math] is of the form either
[TABLE]
where is fixed; or
[TABLE]
with , where is fixed.
*Proof. *Let be the graph isomorphism (4.1) from the adjacency graph of to the bilinear forms graph . For any , we have (where ) if and only if . Thus, is a maximum clique of if and only if has the property that for all in and for all collection of elements of with the property that for all in .
Let be a maximum clique containing [math] in . Write . By Lemma 4.1 and above result, is of the form either (a) consists of all elements of which contain a fixed -dimensional subspace with , or (b) and is the set of all elements of contained in a fixed -dimensional subspace with . In the case (a), it is easy to see that is of the form (4.2). Now, we assume the case (b) happens. Since , by appropriate elementary operations of matrix, we may assume with no loss of generality that \small U^{\prime}=\left(\begin{array}[]{ccc}I_{d-1}&0&0\\ 0&0&I_{m}\\ \end{array}\right). Then . Thus (4.3) holds.
However, the proof of Lemma 4.2 cannot be generalized to the case of . In order to generalize Lemma 4.2 to the case of , we need a new method.
Lemma 4.3
Let and . Then there exists such that is invertible.
*Proof. *Without losing generality, we may assume that . When is an even number, we have \small I_{k}-\left(\begin{array}[]{cc}I_{k/2}&I_{k/2}\\ I_{k/2}&0\\ \end{array}\right) is invertible, and hence this lemma holds. From now on we assume that is an odd number. When , there is \scriptsize B_{1}:=\left(\begin{array}[]{ccc}1&1&0\\ 1&0&1\\ 0&1&0\\ \end{array}\right)\in GL_{3}(\mathbb{Z}_{p}) such that is invertible. When (), by the case of even, there is such that is invertible, and hence is invertible.
Theorem 4.4
Let , and let be a maximum clique of . Then is of the form either
[TABLE]
where and are fixed; or
[TABLE]
with , where and are fixed.
*Proof. *When , this theorem holds by Lemma 4.2. By [17, Theorem 3.6], this theorem holds if . Thus, from now on we assume that and . We prove this theorem by induction on . Assume that this theorem holds for . We prove that it holds for as follows.
Let be a maximum clique of . By (3.6) we have . By the bijection , we can assume that contains [math] (i.e., ).
Let be the natural surjection (2.2). For any vertex in , let denote the preimages of in , i.e., . Suppose is the set of all different elements in . Then is a clique of . It follows from (3.4) that
[TABLE]
Clearly, has a partition into cliques
[TABLE]
where , . Thus
[TABLE]
Let and let
[TABLE]
where is a clique in , . Write where , , . By Lemma 2.7, is a clique of , . By (3.6), we get
[TABLE]
Thus , and hence . By (4.6), we obtain
[TABLE]
Therefore, is a maximum clique containing [math] in .
By Lemma 4.2, we have either \small\pi({\cal M})=P_{0}\left(\begin{array}[]{c}\mathbb{Z}_{p}^{(d-1)\times n}\\ 0\\ \end{array}\right), or with , where are invertible matrices over . Thus, contains a matrix of inner rank , such that either or . Using appropriate elementary operations of matrix, without losing generality, we may assume that
[TABLE]
Since \scriptsize E=P_{0}\left(\begin{array}[]{c}X_{1}\\ 0\\ \end{array}\right) where , or with , where , it follows that \scriptsize P_{0}=\left(\begin{array}[]{cc}P_{11}&*\\ 0&*\\ \end{array}\right) or \scriptsize Q_{0}=\left(\begin{array}[]{cc}Q_{11}&0\\ &*\\ \end{array}\right), where . Thus, is of the form either
[TABLE]
or
[TABLE]
By (4.7)–(4.9), it is easy to see that
[TABLE]
Thus
[TABLE]
It follows that is a maximum clique of , . In other words, there exists a maximum clique of such that
[TABLE]
By the induction hypothesis, is of the form either (4.4), or (4.5) with . Thus,
[TABLE]
where , \scriptsize\left(\begin{array}[]{c}P_{t1}\\ P_{t2}\\ \end{array}\right)\in\mathbb{Z}_{p^{s-1}}^{m\times(d-1)} has a left inverse, has a right inverse, and , .
Let . Then , is of the form (4.14) and contains [math]. By (4.15), we have either
[TABLE]
or
[TABLE]
Suppose (4.16) holds. Since (4.10) and \small\left(\begin{array}[]{cc}0&P_{11}Yp\\ 0&P_{12}Yp\\ \end{array}\right)\sim\left(\begin{array}[]{cc}I_{d-1}&0\\ 0&0\\ \end{array}\right), \small\rho\left(\begin{array}[]{cc}-I_{d-1}&P_{11}Yp\\ 0&P_{12}Yp\\ \end{array}\right)\leq d-1 for all . By (2.7), we get that and is invertible. Therefore
[TABLE]
Suppose (4.17) holds. We have similarly that
[TABLE]
Thus, is of the form either (4.18) or (4.19) with .
We distinguish the following two cases to prove this theorem.
Case 1. is of the form (4.11).
First, we show that (4.18) holds. Let \scriptsize D=\left(\begin{array}[]{cc}0&I_{d-1}\\ 0&0\\ \end{array}\right)\in\pi({\cal M}). By (4.14), there is a maximum clique of such that
[TABLE]
Suppose that is of the form (4.19) with . Then \small\left(\begin{array}[]{cc}X_{1}p&I_{d-1}+X_{2}p\\ X_{3}p&X_{4}p\\ \end{array}\right)\sim\left(\begin{array}[]{cc}Xp&0\\ Wp&0\\ \end{array}\right) for all and . Thus, we can choose and such that
[TABLE]
where . By (2.7), this is a contradiction. Thus must be of the form (4.18).
Let be the -th column of . By (4.14) and (4.15), there is a maximum clique of such that
[TABLE]
Let be any permutation matrix over . Then . Write , . Using (4.18), we get
[TABLE]
Thus
[TABLE]
where . It follows from (2.7) that , and hence the matrix has columns to be zeros. Since the permutation matrix is arbitrary, every column of must be zero, and hence . Then
[TABLE]
Since \small\left(\begin{array}[]{cc}X_{1}p&e_{1}+Y_{3}p\\ 0&Y_{4}p\\ \end{array}\right)\sim\left(\begin{array}[]{cc}I_{d-1}&0\\ 0&0\\ \end{array}\right), it is easy to see that . Therefore, we obtain that
[TABLE]
Similarly, we can prove that
[TABLE]
Let . By (4.14), there is a maximum clique of such that
[TABLE]
By (4.18), we have \bordermatrix{&{}_{d-1}&{}_{n-d}&{}_{1}\cr&A+X_{1}p&Y_{1}p&Z_{1}p\cr&X_{2}p&Y_{2}p&Z_{2}p\cr}\ \sim\bordermatrix{&{}_{d-1}&{}_{n-d}&{}_{1}\cr&X_{1}p&Y_{1}p&Z_{1}p\cr&0&0&0\cr}. Thus \small\rho\left(\begin{array}[]{ccc}A&0&0\\ X_{2}p&Y_{2}p&Z_{2}p\\ \end{array}\right)\leq d-1, and hence and . On the other hand, from (4.21) we get
[TABLE]
Consequently \small\rho\left(\begin{array}[]{ccc}A&0&-e_{i}\\ X_{2}p&0&0\\ \end{array}\right)=\rho\left(\begin{array}[]{ccc}A&0&-e_{i}\\ 0&0&X_{2}A^{-1}e_{i}p\\ \end{array}\right)\leq d-1, and hence by (2.7), . Therefore . Then we obtain
[TABLE]
Let . Write . By (4.14), there is a maximum clique of such that
[TABLE]
From (4.21) we have \bordermatrix{&{}_{d-1}&{}_{n-d}&{}_{1}\cr&A+X_{1}p&Y_{1}p&e_{i}+Z_{1}p\cr&X_{2}p&Y_{2}p&Z_{2}p\cr}\ \sim\bordermatrix{&{}_{d-1}&{}_{n-d}&{}_{1}\cr&X_{1}p&Y_{1}p&e_{i}+Z_{1}p\cr&0&0&0\cr}, which implies that and . By (4.18), we get \bordermatrix{&{}_{d-1}&{}_{n-d}&{}_{1}\cr&A+X_{1}p&Y_{1}p&e_{i}+Z_{1}p\cr&X_{2}p&0&0\cr}\ \sim\bordermatrix{&{}_{d-1}&{}_{n-d}&{}_{1}\cr&X_{1}p&Y_{1}p&Z_{1}p\cr&0&0&0\cr}, and thus
[TABLE]
By (2.7), one has . Using (4.21) again, we get
[TABLE]
and hence
[TABLE]
It follows from that , , which implies that . Thus, for any , we have
[TABLE]
Let . By (4.14), there is a maximum clique of such that
[TABLE]
By Lemma 4.3, exists such that is invertible. Applying (4.22), we get
[TABLE]
which implies that and . On the other hand, from (4.23) we have
[TABLE]
it follows that
[TABLE]
By (2.7), we obtain and hence . Then we have proved that
[TABLE]
In particular, we have
[TABLE]
Using (4.25) and (4.18), similar to the proof of (4.22), we can get that
[TABLE]
Let . Similar to the proof of (4.23), by (4.18) and (4.25) we can prove that
[TABLE]
Using (4.26) and (4.27), similar to the proof of (4.24), we have
[TABLE]
Now, let and . Write . By (4.14), there is a maximum clique of such that
[TABLE]
By (4.28), \left(\begin{array}[]{cc}A+X_{1}p&B_{1}+Y_{1}p\\ X_{2}p&Y_{2}p\\ \end{array}\right)\sim\left(\begin{array}[]{cc}X_{1}p&B_{1}+Y_{1}p\\ 0&0\\ \end{array}\right), hence \small\rho\left(\begin{array}[]{cc}A&0\\ X_{2}p&Y_{2}p\end{array}\right)=\rho\left(\begin{array}[]{cc}A&0\\ 0&Y_{2}p\end{array}\right)\leq d-1, which implies that . By (4.28) again, \left(\begin{array}[]{cc}A+X_{1}p&B_{1}+Y_{1}p\\ X_{2}p&0\\ \end{array}\right)\sim\left(\begin{array}[]{cc}X_{1}p&B_{1}+(0,e_{i})+Y_{1}p\\ 0&0\\ \end{array}\right), and hence \small\rho\left(\begin{array}[]{ccc}A&0&-e_{i}\\ X_{2}p&0&0\end{array}\right)=\rho\left(\begin{array}[]{ccc}A&0&-e_{i}\\ 0&0&X_{2}A^{-1}e_{i}p\end{array}\right)\leq d-1, . It follows that , , thus . Therefore, we get that
[TABLE]
for all and .
Finally, let and . Write . By (4.14), there is a maximum clique of such that
[TABLE]
By Lemma 4.3, there is such that is invertible. Applying (4.29),
[TABLE]
and hence \small\rho\left(\begin{array}[]{cc}B-A&0\\ X_{2}p&Y_{2}p\end{array}\right)=\rho\left(\begin{array}[]{cc}B-A&0\\ 0&Y_{2}p\end{array}\right)\leq d-1. Thus . By (4.29) again, we have
[TABLE]
and hence
[TABLE]
Consequently, , . Thus it is clear that . Therefore, we obtain that
[TABLE]
for all and . Then we have proved that
[TABLE]
Case 2. is of the form (4.12) with .
Let . Then is a maximum clique of . By (2.5), is of the form (4.11). By Case 1, we have that {}^{t}{\cal M}=\left(\begin{array}[]{c}\mathbb{Z}_{p^{s}}^{(d-1)\times m}\\ 0\\ \end{array}\right). Thus .
4.2 Cores of and the complement of
A graph is a core [14] if every endomorphism of is an automorphism. A subgraph of a graph is a core of [14] if it is a core and there exists some homomorphism from to . Every graph has a core, which is an induced subgraph and is unique up to isomorphism [14, Lemma 6.2.2].
Lemma 4.5
(see [23, Lemma 2.5.9])* Let be a graph. Then the core of is the complete graph if and only if .*
Corollary 4.6
Let . Then the core of is a maximum clique of , and is not a core.
*Proof. *Let . By (3.11), we have . By Lemma 4.5, the core of is a maximum clique, and hence is not a core.
Let denote the complement of a graph . Then , , and . Moreover, .
Theorem 4.7
Let where . Then . Moreover, the core of is a maximum clique of , and is not a core.
*Proof. *Let be a largest independent set of , where . Let
[TABLE]
By (3.6), is a maximum clique of . Put , . Then are maximum cliques of , and for all . By Theorem 3.3, we get . Thus, has a partition into maximum cliques: . Since are largest independent sets of , has a partition into largest independent sets: . Hence . By , we obtain . By Lemma 4.5, we have similarly that the core of is a maximum clique, and hence is not a core.
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