The smallest spectral radius of bicyclic uniform hypergraphs with a given size
Abstract
Identifying graphs with extremal properties is an extensively studied topic in spectral graph theory. In this paper, we study the log-concavity of a type of iteration sequence related to the -normal weighted incidence matrices which is presented by Lu and Man for computing the spectral radius of hypergraphs. By using results obtained about the sequence and the method of some edge operations, we will characterize completely extremal -graphs with the smallest spectral radius among bicyclic hypergraphs with given size.
Keywords: Bicyclic hypergraph, Spectral radius, Weighted incidence matrix, Log-concave sequence.
AMS subject classification 2010: 05C65, 05C50, 15A18.
1 Introduction
A hypergraph consists of a set of vertices and a collection of subsets of . We call a member of a hyperedge or simple an edge of . A hypergraph is -uniform if all of its edges are -subsets of . We also simply call a -uniform hypergraph a -graph for brevity. Throughout this paper, we focus on simple -uniform hypergraphs with .
A hypergraph is a sub-hypergraph of if and , and is a proper sub-hypergraph of if or .
Let be a vertex of . We use to denote the set consisting of edges of which are incident to , also written as for brevity. The cardinality of is defined as the degree of the vertex , denoted or . A vertex of degree one is called a pendant vertex. For , if there exists at least pendant vertices, we say is a pendant vertex of ; if there exists at least one vertex of degree greater than 2 or three vertices of degree greater than 1, we say is a branch edge of .
In hypergraph theory, the most common definition of a cycle in a hypergraph is given by Berge (see [1]).
A Berge cycle of length is a hypergraph consisting of distinct hyperedges such that there exist distinct vertices satisfying that for each and .
A Berge path of length is a hypergraph consisting of distinct edges such that there exist distinct vertices satisfying that for .
In graph theory, an internal path of a graph is a sequence of vertices such that all are distinct, except possibly ; the vertex degrees satisfy , and for .
Let be a simple graph. The -th power hypergraph of is the -uniform hypergraph resulting from adding new vertices to each edge of . For the ordinary path and cycle with edges, the -th power of them are called loose path and loose cycle with length , denoted by and , respectively.
In the following, we will give a general definition of internal path for hypergraph.
Definition 1.1.
For hypergraph , let be a vertex edge alternating sequence of such that is a loose path of hypergraph and , (not necessarily distinct).
-
(1).
If and are both branch edges, then is called hyper-internal path of and is called internal loose path.
-
(2).
If is a hyper-internal path and one of and contains no vertex with degree more than 2, then is called hypo-internal path of .
In graph theory, the cyclomatic number of connected graph is , which corresponds to the number of independent (in the sense of linear independence in the so-called cycle space) cycles in . In [2], Fan et al. present a generalization of cyclomatic number for -uniform graph. Let be a -graph with vertices, edges, and connected components. The cyclomatic number of is denoted as and defined as . The connected hypergraph with is called a -cyclic hypergraph. In particular, -cyclic hypergraph, -cyclic hypergraph, -cyclic hypergraph, are called supertree, unicyclic -graph, bicyclic -graph, respectively.
A very important topic on spectral hypergraph theory is the characterization of hypergraphs with extremal values of the spectral radius in a given class of hypergraphs. The spectral radii of hypertrees are well studied in the literatures, see [3, 4, 5]. In [6], Linyuan Lu and Shoudong Man research the connected hypergraphs with small spectral radii and present the -normal labeling method for comparing the spectral radii of connected -graphs.
In [2, 7], hypergraphs that attain the largest spectral radii among all unicyclic and bicyclic k-graphs are investigated. Recently, Zhang et al. determine the uniform hypergraphs of size with the first two smallest spectral radii [8].
Motivating by the preceding work on maximizing and ordering spectral radius, we will consider spectral extremal problems for bicyclic hypergraphs with given size in this paper.
The remainder of this paper is organized as follows: In Section 2, we give some basic definitions and results for tensor and spectra of hypergraphs. In Section 3, a kind of iteration sequence of möbius transformation, which related with some weighted incidence matrix of hypergraph, is researched. We present an explicit expression of the sequence and some property on the convexity and log-concavity of the sequence is researched. In Section 4, effects on the spectral radius of hypergraph by graph operations are researched. In Section 5, the first smallest spectral radius of bicyclic hypergraph are determined.
2 Preliminaries
The definition of eigenvalues of a tensor was proposed in [9, 10], independently. Let be an order dimension tensor. If there exists a number and a nonzero vector such that
where is a vector with -th entry , then is called an eigenvalue of , is called an eigenvector of corresponding to the eigenvalue . The spectral radius of is the maximum modulus of the eigenvalues of , denoted by .
In 2012, Cooper and Dutle [11] defined the adjacency tensors for -uniform hypergraphs.
Definition 2.1 ([11]).
The adjacency tensor of a -uniform hypergraph is defined to be an order dimension tensor with entries such that
Theorem 2.1.
Suppose that is a uniform hypergraph, and is a sub-hypergraph of . Then . Furthermore, if in addition is connected and is a proper sub-hypergraph, we have .
Definition 2.2.
[6] A weighted incidence matrix of a hypergraph is a matrix such that for any vertex and edge , the entry if and if .
Let () be a Berge cycle of . For a weighted incidence matrix of , if
we say that is consistent with . If is consistent for any Berge cycle of , we say that is consistent for .
Definition 2.3.
[6] A hypergraph is called -supernormal if there exists a weighted incidence matrix B satisfying
-
(1).
, for any ,
-
(2).
, for any .
If both equalities hold in above inequalities, is called -normal. Otherwise, is called strictly -supernormal. Furthermore, if is consistent, is called consistently -supernormal.
Definition 2.4.
[6] A hypergraph is called -subnormal if there exists a weighted incidence matrix B satisfying
-
(1).
, for any ,
-
(2).
, for any .
If one of above inequalities is strict, is called strictly -subnormal.
Theorem 2.2.
[6] Let be a connected -uniform hypergraph. Then the following results hold:
-
(1).
is the spectral radius of if and only if is consistently -normal with .
-
(2).
If is -subnormal, . Moreover, if is strictly -subnormal, .
-
(3).
If is strictly and consistently -supernormal, .
Lemma 2.1.
Let be a connected -uniform hypergraph, and be the principal eigenvector of . Define the weighted incidence matrix such that
Then is consistently -normal, where .
In the sequel, the weighted incidence matrix of hypergraph defined in Lemma 2.1 is denoted by .
Definition 2.5.
Let be an internal path of length between and in hypergraph . Let and , is a weighted incidence matrix of . Write .
We say that is -normal on , if satisfies the following conditions:
-
(1).
, for any ,
-
(2).
, for any .
Lemma 2.2.
Let be two edges of -uniform hypergraph and be a weighted incidence matrix of .
-
(1).
Let is a - internal loose path of . If is -normal for and , then is consistent for cycle .
-
(2).
Let is a - internal loose path and is a - internal loose path of . For , if is -normal for and , then is consistent for cycle .
Proof.
(1). Suppose and . Let () and , . Since is -normal for , we have for any .
For Berge cycle , we have
So is consistent for cycle .
(2). Using similar arguments the result can be deduced from the conditions. ∎
3 On a kind of sequence obtained by iteration of Möbius transform
In this section, we will focus on the convexity and log-concavity of some sequences which are related with the -normal weighted incidence matrices of hypergraphs. Proposition 3.1 of this section will play a key role in the proof of our main result (Theorem 5.4) of this paper.
Let be a connected -graph and be a loose path in -graph . Let be a -normal weighted incidence matrix. Take () and . Then we have: for .
Hence, is finite portion of the following bi-infinite sequence such that
(3.1) |
where , which is a special type of Möbius transform (see Section 1.2 of [13]).
is called characteristic equation of the iterative sequence . The roots of are called the characteristic roots of the sequence .
When , have two distinct positive roots, denoted by . Suppose . Let . Then can be written in the following form:
Since , can be rewritten as:
For sequence (3.1), if there exists some integer such that , then , and .
If the sequence (3.1) is positive sequence and there exist integers such that , we call that is symmetric.
It is obvious that is symmetric when .
Lemma 3.1.
Let be the sequence mentioned above.
-
(1).
If and , then
where .
-
(2).
If , then is strictly increasing and .
-
(3).
If , then is strictly decreasing and .
-
(4).
If , then there exists such that and is positive decreasing sequence.
Proof.
(1). When , , for any integer , The result holds.
When , as mentioned above, be characteristic roots of sequence . By the formula (8) of [14], we have:
(2). Since , have two distinct positive roots, denoted by as before. So . Because are strictly increasing functions in the interval , we have
Therefore, and .
So , , . follows.
Since , we have for any .
Hence, is strictly increasing bounded sequence. And exists. Suppose . It is clear that . Next, we take limits as on both sides of . We have . So is a root of . Since , follows. That is, .
(3) If , then and . So is a decreasing bounded positive sequence. So exists, denoted by . Furthermore, will be a root of . From , we have . Since is the maximal root of , we have .
(4) If , then and . There must exist such that . Otherwise, is a bounded positive sequence. So exists, denoted by . Furthermore, will be a root of . From , we have , which contradicts that is the minimal root of . The result follows.∎
Let
For the sake of brevity, take Then
Suppose the sequence is symmetric. Without loss of generality, suppose (, ). Take . Then is an iteration sequence of with initial value . By (1) of Lemma 3.1, we have for all .
For , . So . Namely, is a root of the equation .
Furthermore, we have the following conclusion:
Theorem 3.1.
Let be the symmetric sequence mentioned as above.
-
(1).
For any , we have .
-
(2).
If , then is strictly increasing bounded sequence with for any and .
-
(3).
For , is the largest root of and
Proof.
(1). It is sufficient to show that if there exist integers such that , then .
For , we have
. So .
Since , holds for any .
(2). Take . From (1), holds. So
Hence, and .
The result follows from (2) of Lemma 3.1.
(3). Since is a root of , By (1) of Lemma 3.1, is a root of the following equation:
which has the same roots as the following quadratic equation
Let be the discriminant of above quadratic equations. Then
By the quadratic formula we obtained the two roots of above equation:
Next, we will complete the proof of (2) by showing that .
Since , to show , it is efficient to prove that .
Since , we have
Since , the last condition holds. So
This formula can be rewritten as
In the sequel, we always write
and
Consequently, and .
From (1) and (3) of Theorem 3.1, we have
(3.2) |
Furthermore, we have the following Corollary.
Corollary 3.1.
is the symmetric sequence defined as (3.1). Without loss of generality, suppose (). Then
for , can be expressed as:
The remainder of this section is dedicated to the study of the convexity of and the log-concavity of .
Definition 3.1.
A function is log-concave on , if for all and the function is concave on .
It is well known (see [15]) that
Theorem 3.2.
Let be positive function on . Then the function
is Schur-concave if and only if is log-concave.
Theorem 3.3.
Let and be as defined above, then
-
(1).
is strictly decreasing and convex in the interval ;
-
(2).
is strictly increasing and concave in and log-concave in .
Proof.
(1). For , by simple computation, we have
and
Since , and on . So is strictly decreasing and convex in the interval .
(2). Since , from (1), it follows that is strictly increasing and concave in the interval .
Since
Let . We have
and
For , , we have . So is log-concave in . ∎
Proposition 3.1.
Suppose are real numbers such that , then
-
(1).
If , we have ;
-
(2).
.
4 The effect on the spectral radii of uniform hypergraphs by some graph operations
In the study of spectral graph theory, the effects on the spectrum are observed when some operations, such as edge moving, edge subdividing, are applied to the graph. In this section, we study the effects on spectral radii of -graphs under the operations:vertex-splitting and vertex-releasing operations.
Definition 4.1 (Edge moving operation [16]).
For , let be a -uniform hypergraph with and for such that and for , where are not necessarily distinct. Let for . Suppose that for . Let . Then we say that is obtained from by moving edges from to .
The following Lemma is a special case of Corollary 2.1 in [16].
Lemma 4.1.
Let are non-pendant vertices in an edge of connected uniform hypergraph with for . Let be the hypergraph obtained from by moving edges from to and , then
Corollary 4.1.
Let be a connected -graph, (), and . Take , where . Then .
Proof.
Since and are vertices with degree at least 2 in . Let be the hypergraph obtained by moving from to , then . By Lemma 4.1, we have . ∎
Definition 4.2 (vertex-splitting operation).
Let be a vertex of a hypergraph and be the hypergraph obtained by replacing with an edge in such a way that some vertices adjacent to are now adjacent to and the rest are adjacent to . and . Then is said to be obtained from by splitting vertex .
Lemma 4.2.
Let be a vertex with degree 2 of connected -graph . Suppose and be the -graph obtained from by splitting vertex . Let . If , then
and .
Proof.
Suppose that are new edges obtained from and and be the new edge of which obtained from during the process of splitting vertex . Then and . Take be a weighted incidence matrix of such that
Then and , we have
Since , holds. Consequently, .
So is -subnormal. By (2) of Theorem 2.2, we have and . ∎
Proposition 4.1.
Let be an edge of connected -graph with and be the -normal weighted incidence matrix of , then
-
(1).
;
-
(2).
if , then .
Proof.
(1). Let and .
By the definition of -normal weighted incidence matrix, we have:
and .
So .
Similarly, .
Since , we have .
(2).
From (1), we have
.
∎
Let
where . Then is the unique root of the following equations in the interval :
(4.1) | |||
(4.2) |
Claim 1.
For , take be the two roots of . We have
Proof.
Since , we have . Then
Since is the unique root of equation (4.1) in the interval , the claim follows. ∎
Lemma 4.3.
Let be a loose cycle with vertex and . We use , to denote the -graph obtained from by attaching a pendent edge to vertex and , respectively. We have
Proof.
Let , and for .
Since is a proper subgraph of , we have .
Then we have and is a symmetric sequence. According to Theorem 3.1, . Let be -graph obtained from by splitting vertex . It is clear that . By Lemma 4.2, we have . Namely, is a strictly increasing bounded sequence. So exists. Suppose . Let . Because is the -normal weighted incidence matrix of , from Theorem 3.1 it follows that is the root of the following equation:
Theorem 4.1.
Suppose .
Let be two edges of connected -graph and , both contains at least three vertices with degrees exceeding 1. is an internal path of between and .
Let be a -graph obtained from by splitting vertex .
If , then .
Proof.
Suppose .
Write for and
.
Take . From Proposition 4.1, .
Since , by Claim 1, we have
.
Similarly, .
According to Lemma 4.2, to complete the proof, it suffices to show . That is, .
Definition 4.3 (Vertex-releasing operation).
Let be an edge of connected -graph and () be all vertices with degrees at least 2 in . Take (), where are new vertices. Let be hypergraph such that . We say that is obtained from by releasing vertex of .
Theorem 4.2.
Let be an edge of connected -graph and () be all vertices with degrees at least 2 in . Take be -graph obtained from by releasing some vertices of . We have
Proof.
Let and .
Suppose and .
Let be the consistently -normal weighted incidence matrix of .
Take be a weighted incidence matrix of such that
Then, for any in , .
For any in ,
and
for ,
and .
Hence, is strictly -subnormal weighted incidence matrix of .
The result follows from Theorem 2.2.
∎
Theorem 4.3.
Let be a connected -graph with . If is not loose cycle, then .
Proof.
Let be a minimal -cyclic subgraph of with .
Case 1. is a loose cycle.
When there exist a subgraph of isomorphic to one of the following two graphs: and , where is some integer more than one, by Lemma 4.3, . Otherwise, since is connected and is not loose cycle, there must exist such that . Let be hypergraph with and . A hypergraph can be obtained by application of vertex-releasing operation to vertices in of degree more than one of such that is isomorphic to or . By Theorems 4.2, 2.2, we have .
Case 2.
is not loose cycle.
Since , must be hypergraph with and .
Write . Then we have . ∎
Corollary 4.2.
Let be a connected -graph with and is a vertex with degree 2 of some internal path of . Let be a -graph obtained from by splitting vertex . Then .
5 Extremal hypergraphs in with the smallest spectral radius



Let be a -cyclic hypergraph. The base of , denoted by , is the (unique) minimal -cyclic subgraph of . It is easy to see that can be obtained from by consecutively deleting pendent edges.
It is known that there are three types of bases of bicyclic graphs (see Fig. 1). is obtained from two vertex-disjoint cycles and by identifying vertex of and vertex of , is obtained from two vertex-disjoint cycles and by joining vertex of and vertex of by a path of length , () is obtained from three pairwise internal disjoint paths of lengths from vertices to . Note that is exactly .
The bicyclic graph containing as its base is called a -graph.
For a hypergraph , we define the incidence graph to be the bipartite graph with the vertex set and the edge set
The graph is also called the König representation of hypergraph .
It is easy to see that the number of Berge cycles of hypergraph and the number of cycles of its König representation is same.
Proposition 5.1.
Let be a connected -uniform hypergraph, then .
Proof.
Suppose be a connected -uniform hypergraph with order and size . Since By the definition of , we have and . So . ∎
Inspired by the above result, the definition of the cyclomatic number for -graph can be generalized to general hypergraph as follows: The cyclomatic number of general hypergraph , denote by , is defined as the cyclomatic number of the König representation of , namely .
Let be a -cyclic hypergraph and be hypergraph obtained by adding some new vertices with degree 1 to some edges of . It is obvious that .
Let be a -graph and be an edge of , where . Subdividing an edge means replacing edge by two edges and , where are new added vertices.
Let denote the set consisting of all bicyclic -graphs with size . In the remaining part of this section, we will characterize the extremal hypergraph with the smallest spectral radius among bicyclic -graph with given size.
Let consist of all the minimal bicyclic -graphs () with edge number at most . Let , .
Lemma 5.1.
Let be hypergraph in with the smallest spectral radius. Then is hypergraph in .
Proof.
Let be hypergraph in with the smallest spectral radius. We will show with .
Assume . Since the König representation is bicyclic graph, is one of , and and there exists vertex with which is a vertex of with . Since , there exist and such that and . From Corollary 4.1, there exists -graph () with such that . Namely, , which is a contradiction to our assumption on . So . If , let be a hypergraph obtained by splitting some vertex with . By Corollary 4.2, and , a contradiction. So and .
Assume . Then we have is proper subgraph of and . By Theorem 2.1 we have , which is a contradiction to our assumption on since . Therefore, is hypergraph in with size . ∎


Let be hyperedges with and .
Take such that , . Let be non-negative integers. We denote by the -graph (See (a) of Fig. 3) obtained from and by joining vertex pairs , and with three internal loose paths of lengths , respectively. is also referred to as -type hypergraph.
Let be the -graph (See (b) of Fig. 3) obtained from and by joining vertex pairs , , with three internal loose paths of lengths , respectively. is also referred to as -type hypergraph.
Let be distinct vertices in hyperedge . We denote by the -graph obtained from by joining vertex pairs , with two internal loose paths of lengths , respectively. is also referred to as -type hypergraph.
Suppose . Those vertices with degree more than 3 in must be edges of . It is clear that
-
(1).
when is isomorphic to some (), is -type -graph,
-
(2).
when is isomorphic to some , is -type -graph,
-
(3).
when is isomorphic to some , is -type -graph.
The bicyclic hypergraphs in can be partitioned into the following three sets:
Lemma 5.2.
For any positive integers , we have
Proof.
Let .
Take .
Suppose is the consistently -normal weighted incidence matrix of , then we have .
Let , . Take , .
Then .
By exchanging the values of , and , , respectively, a consistently -normal weighted incidence matrix of is obtained from . Therefore, and . ∎
Theorem 5.1.
Let be positive integers such that . Then are the extremal hypergraph with the smallest spectral radius in , respectively.
Proof.
Suppose is the extremal hypergraph with the smallest spectral radius in . Then (). Without loss of generality, Let . Take , . Let be the iterated sequence of with . By the symmetry of , holds. So is a symmetric -normal sequence. By Corollary 3.1, we have . If , then . Since , By Corollary 3.1, there exists a weighted incidence matrix of which satisfies the following conditions.
-
(1).
When , for the internal loose paths -, - and - of , is -normal, -normal and -normal. And
Without loss of generality, suppose . Since , .
Since is log-concave on , From , , we have
when and
when .
By (2) of Proposition 3.1, . Hence, .
-
(2).
When ,
for each , the internal loose path - of , are -normal and .Since is log-concave in ,
.
Therefore, for , we have
,
,
.
By (1) of Lemma 2.2, is consistent with all cycles of for .
That is, is consistently -supernormal, by Lemma 2.2, , which is a contradiction to our assumption on .
So is the extremal hypergraph with the smallest spectral radius in for .∎
Theorem 5.2.
Let be positive integers such that , . Then is the hypergraph with the smallest spectral radius in .
Proof.
Let be hypergraph in with the smallest spectral radius. By similar arguments of proof of Lemma 5.1, . That is, is a type hypergraph. Without loss of generality, suppose and . Let , . Suppose that is the iteration sequence of with . By the symmetry of , we have and is a -normal sequence. From Corollary 3.1, .
Since , according to Corollary 3.1, we can build a weighted incidence matrix of
such that
is -normal for the loose path - and -normal for -,
and
Then , and , .
For , .
By (1) of Lemma 2.2, is consistent for any cycle of for .
Therefore, is consistently -supernormal. According to (3) of Theorem 2.2, , which is a contradiction to our assumption on .
Hence, is the hypergraph with the smallest spectral radius in . ∎
Theorem 5.3.
Let integers such that . Take hypergraphs , . Then .
Proof.
Set . Let be the iteration sequence of with . By the symmetry of , . So is a symmetric -normal sequence. By Corollary 3.1, we have .
Since , by Corollary 3.1, there exists a weighted incidence matrix of such that
is -normal for - and -normal for -.
And,
, , .
Since , , .
For ,
By (2) of Theorem 2.2, is -subnormal. holds. ∎
Theorem 5.4.
Let and be the hypergraph with the smallest spectral radius in , respectively. Then
-
(1).
,
-
(2).
are the hypergraphs with the smallest spectral radius in .
According to those results obtained, we can compute the values of spectral radii of -graphs with the smallest spectral radius.
Let be the smallest spectral radius of bicyclic -graph with size and . Suppose
Let be the unique positive root of equation
Then .
As a example, we take to be a -graph with the smallest spectral radius in for . Let and . Then is the positive root of the following equation
By direct calculation, we have and . Hence, .
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