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Abstract

In the design of multiprocessor systems, evaluating the reliability of interconnection networks is a critical aspect that significantly impacts system performance and functionality. When quantifying the reliability of these networks, extra connectivity and extra diagnosability serve as fundamental metric parameters, offering valuable insights into the network’s resilience and fault-handling capabilities. In this paper, we investigate the 1-extra connectivity and 1-extra diagnosability of the n-dimensional enhanced folded hypercube-like network. Through analysis, we show that the 1-extra connectivity of this network is 2n+2. Moreover, for n>5, we determine its 1-extra diagnosability under both the PMC model and the MM model to be 2n+3. These results show that as the dimension n increases, both the 1-extra connectivity and 1-extra diagnosability of the network approach approximately twice the value of traditional diagnosability metrics. This provides quantitative insights into the reliability properties of the enhanced folded hypercube-like network, contributing to a better understanding of its performance in terms of connectivity and fault diagnosis.

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1. Introduction

With the advancement of high-performance computing technology, the number of processors in interconnection networks is steadily growing. As the number of processors increases, it becomes highly likely that processors within interconnection networks will encounter failures. Reliability has been a crucial issue in interconnection networks. These networks’ reliability can be evaluated using two metrics: connectivity and diagnosability. In the study of network properties, we often use a graph G to represent the underlying network topology. A multiprocessor network typically consists of a number of processors and communication links. In graph G, we utilize nodes to represent processors and edges to represent the communication channels between them. G’s connectivity, denoted by κ(G), is defined as the smallest number of nodes that, when removed, cause the remaining graph to become disconnected or reduce to a trivial graph. Based on this definition, it is theoretically possible for all the neighbors of a specific node to fail. However, the probability of all neighbors of a given node failing simultaneously is extremely small. As the network size grows, the traditional concept of connectivity becomes insufficient to accurately reflect the network’s fault-tolerance ability. To address this issue, many researchers have proposed various connectivity metrics under different constraints [1,2,3].

Another generalized concept of connectivity is the g-extra connectivity, which was proposed in [4]. It is also a new concept introduced to improve network fault tolerance. G’s g-extra connectivity κg(G) refers to the minimum number of nodes that need to be removed to make G disconnected such that each remaining component contains at least g+1 nodes. Subsequently, Lv et al. [5] explored the g-extra connectivity of the (n,k)-star network; Yang et al. [6] proved the g-extra connectivity of hypercubes; and Zhu et al. [7] gave the g-extra connectivity of BC networks. Since BC networks include hypercubes, Möbius cubes, etc., the g-extra connectivity of these networks can be derived from the research results of Zhu et al. [7]. For the k-ary n-cube, when k=2, it is the n-dimensional hypercube network; when k=3, it contains triangles; when k>4, it does not contain triangles. Therefore, different values of g-extra connectivity can be obtained for different parameter ranges. Thus, Liu et al. [8] respectively studied the g-extra connectivity of k-ary n-cubes under different conditions k4 and k=3. The DQcube network is a composite network in which each node is replaced by an identical hypercube to form a new undirected disk network. Zhang and Meng [9] studied and obtained the g-extra connectivity of this network. Li et al. [10] obtained the g-extra connectivity of the data center network DCell; Lv et al. [11] studied the g-extra connectivity of the data center network BCDC; Zhou et al. [12] proved the g-extra connectivity of a class of recursive matching networks.

The process of identifying faulty processors by test results between processors is called system-level diagnosis. A system is termed t-diagnosable if the number of faulty processors does not exceed t, and all faulty units can be identified without replacement. The maximum number of faulty processors that a system can identify is denoted as its diagnosability [13]. Two well-known models have been proposed by various diagnosis strategies. The PMC model (P-M), first proposed by Preparata et al. in [14], is the earliest diagnostic framework. Under this model, two processors can mutually test each other if connected by an edge. Later, Sengupta and Dahbura [15] expanded the MM model and introduced the MM model (M-M). In the M-M, a processor adjacent to two other processors will perform tests on those two neighboring processors.

Generally, a system’s diagnosability corresponds to the neighborhood size of a specific node, which often limits the number of faulty processors it can identify. Moreover, the likelihood that all neighbors of a processor in the system fail simultaneously remains relatively low. Therefore, Lai et al. [16] devised a novel diagnosability measure, namely, conditional diagnosability. This measure is premised on the assumption that all the adjacent nodes of any given node cannot be faulty simultaneously. Thus, another classical generalization of diagnosability is the g-extra diagnosability (g-ED), first proposed in [17]. This concept imposes a constraint that each remaining component must contain at least g+1 nodes after removing faulty nodes, thereby further enhancing the system’s diagnostic capability. As a node-symmetric and edge-symmetric recursive network, the hypercube was proven by Zhang and Yang [17] to have g-ED under the P-M and M-M within specific ranges of g. Due to the complex structure of permutation graphs, studying their g-ED for general g is highly challenging. Consequently, Lin et al. [18] investigated the g-ED of permutation graphs for g=1,2,3 under both the P-M and M-M. Wang [19] further studied the g-ED of the Petersen graph under the M-M. For more research on other diagnosabilities, please refer to references [20,21].

Currently, many parallel computers with hypercubes as their underlying topological structure have been commercialized, such as Caltech’s Cosmicu [22] and Intel’s IPSC/2 [23]. With the continuous advancement of research, in recent years, data center networks based on hypercubes have also been emerging, such as the HSDC data center network [24] and BCDC data center network [25]. In the quest for higher-performance topological structures, folded hypercube-like networks, as a variant of hypercubes, have been proposed [26]. They include folded hypercubes, with a diameter about half that of hypercubes, and retain the hypercubes’ symmetry. Recently, enhanced folded hypercube-like networks (EFHNs), as an extension, have been put forward [27]. They keep the properties of the original folded hypercube-like networks, with recursive construction via hypercube-like structures. Their diameter is usually smaller, meaning shorter data transmission paths, lower communication latency, and higher parallel computing efficiency, meeting the high-performance needs of parallel computers in large-scale data-processing and complex tasks. Thus, studying their reliability is crucial. That is, exploring other properties of this network has significant research value. In this paper, we aim to explore the extra connectivity and the extra diagnosability of EFHNs under some conditions.

The structure of this paper is outlined as follows. Section 2 presents relevant notations, two diagnostic models, and the definition of EFHNs. In Section 3, we analyze the 1-extra connectivity of this network. Section 4 investigates its 1-ED under the P-M and M-M. Finally, we conclude the paper.

2. Preliminaries

This section presents the interpretations of certain symbols, details of two diagnostic models, and the definition of the EFHN. To simplify the discussion, the terms “networks” and “graphs”, along with “nodes” and “processors”, are used synonymously throughout the paper.

2.1. Notations

In this section, we present several basic terms and symbols that are applied in the entire paper. For convenience, we adopt the representation of a graph G=(V(G),E(G)) to illustrate the network topology. In this notation, V(G) indicates the set of nodes in graph G, whereas E(G) stands for the set of edges in G. Additionally, the number of elements in the set V(G), symbolized by |V(G)|, is called the order of graph G, and the value of |E(G)| is defined as the size of G. It is crucial to note that throughout this paper, our focus is solely on simple, finite, and undirected graphs, excluding other types of graphs from our discussions.

For any two nodes v1,v1V(G), if there is an edge that links these two nodes, we can claim that k1 and k1 are adjacent, or they are neighbors of each other. This relationship can be symbolized as (v1,v1)E(G). For an arbitrary node v1 in graph G, we denote its neighborhood as NG(v1). This set consists of all nodes that are adjacent to v1. That is, NG(v1)={v1V(G)|(v1,v1)E(G)}. Similarly, when considering any subset of nodes S in graph G, its neighborhood, denoted as NG(S), represents the set of nodes that are adjacent to at least one node within the subset S. More precisely, NG(S)={v1V(G)|v1S, (v1,v1)E(G)}S=v1SNG(v1)S. For v1V(G),SV(G), we define NG[v1]=NG(v1){v1} and NG[S]=NG(S)S. The degree of node v1 in graph G, denoted by dG(v1), refers to the number of edges that are incident to v1. Given that G is a simple graph, we can conclude that dG(v1)=|NG(v1)|. The minimum degree of G is determined by δ(G)=min{dG(v1)|v1V(G)}.

Moreover, for any two nodes v1 and v1 in graph G, the notation |NG(v1)NG(v1)| is used to represent the number of common neighbors between them. For the sake of simplicity in our discussions, we define s={0,1,,s} and [s]={1,2,,s}.

2.2. Two Diagnostic Models

In what follows, we present two well-known diagnostic models. Specifically, these are the P-M and the M-M.

Definition 1

([17]). A node subset N of graph G is a g-extra node subset if and only if every component of GN has more than g nodes.

Definition 2

([17]). G=(V,E) is h-extra t-diagnosable if and only if for each pair of distinct faulty g-extra node subsets N1,N2V(G) such that if |N1|,|N2|t, N1 and N2 are distinguishable. The g-ED of G, denoted as tg(G), is the maximum value of t such that G is g-extra t-diagnosable.

2.2.1. PMC Model

Under the P-M, the testing process is carried out between two neighboring processors. We use the ordered pair (v1,v1)¯ to represent that the node v1 conducts a test on the node v1 and σ(v1,v1) to denote the test result. When the node v1 operates without faults, the test result is reliable. In terms of representing the test results, if the node v1 is fault-free under this circumstance, we assign σ(v1,v1)=0; conversely, if the node v1 has faults, we assign σ(v1,v1)=1.

Theorem 1

([28]). For N1,N2V(G), (N1,N2) is a distinguishable pair of G under P-M if and only if there is a node uV(N1N2) and a node vN1ΔN2 such that (u,v)E (see Figure 1).

2.2.2. MM Model

The MM model involves a node w with two neighbors u and v; w sends the same diagnostic information to u and v, and compares their responses. For convenience, we use σ(w;u,v) to denote the test result. Non-faulty nodes return identical results, while faulty nodes may return different ones. If u and v return the same result, they are considered non-faulty (recorded as σ(w;u,v)=0); if they are different, at least one is faulty (recorded as σ(w;u,v)=1). Additionally, the M-M is a variant of MM that requires every 2-path’s middle node w to perform diagnosis without exception.

Theorem 2

([15]). For N1,N2V(G), (N1,N2) is a distinguishable pair under M-M if and only if one of the following conditions is satisfied (see Figure 2): (1) 

There are two nodes u1,w1V(G)(N1N2) and there is a node v1N1ΔN2 such that (u1,w1)E(G) and (v1,w1)E(G);

(2) 

There are two nodes u1,v1NjN1j and there is a node w1V(G)(NjN1j) such that (u1,w1)E(G) and (v1,w1)E(G) for j{0,1}.

Figure 2

Illustration of distinguishability under the M-M.

[Figure omitted. See PDF]

In summary, how these models work is mainly based on testing the connections between nodes. Under the P-M, if two nodes are connected by an edge, they can test each other. Under the M-M, if a node has two different neighboring nodes, it can test its two neighboring nodes. The specific testing rules for these two modes are described above, respectively. In addition, for ease of understanding, the following is a brief introduction to why the conditions listed in Theorems 1 and 2 are sufficient to guarantee distinguishability.

P-M: (Sufficiency) If v1N1N2 then σ(u1,v1)=0 when N2 is the fault set, whereas σ(u1,v1)=1 when N1 is the fault set. Similarly, if v1N2N1, then σ(u1,v1)=0 when N1 is the fault set, whereas σ(u1,v1)=1 when N2 is the fault set. In either case, σ(N1)(N2)=0.

M-M: (Sufficiency) Suppose that condition (1) is satisfied. If v1N1N2, then σ(w1;u1,v1)=0 when N2 is the fault set, whereas σ(w1;u1,v1)=1 when N1 is the fault set. Hence σ(N1)(N2)=0. A similar argument can be used when v1N2N1. Suppose that condition (2) is satisfied. If v1N1N2 then σ(w1;u1,v1)=0 when N2 is the fault set, whereas σ(w1;u1,v1)=1 when N1 is the fault set. Similarly, if v1N2N1, then σ(w1;u1,v1)=0 when N1 is the fault set, whereas σ(w1;u1,v1)=1 when N2 is the fault set. In either case, σ(N1)(N2)=0.

2.3. The Folded Hypercube-like Network

The definition of the n-dimensional folded hypercube-like network FHn is presented.

Theorem 3

([26]). FHn is defined recursively:

(1) Let X1 be an edge on two nodes labeled by 0 and 1. Let L1={X1}.

(2) Construct two instances of X1: Create X10 by prefixing labels with 0, resulting in an edge connecting nodes labeled 00 and 01. Similarly, form X11 by prefixing labels with 1, yielding edge connecting nodes labeled 10 and 11. Add a perfect matching between X10 and X11 such that no edges from M¯ are included. Denote the resulting graph as X2, which is isomorphic to a 4-cycle (see Figure 1). Define L2={X2}.

(3) To construct X3, duplicate X2 into X20 and X21 by prepending 0 and 1 to all node labels, respectively. Connect these copies with a perfect matching that excludes edges from M¯. The resulting graph X3 is isomorphic to either the 3-dimensional hypercube Q3 or the 3-dimensional crossed cube CQ3. Define L3={Q3,CQ3}.

(4) Construct two graphs by selecting one graph from L3 each, denoted as X30 and X31, where labels are prefixed with 0 and 1 respectively. Note that X30 and X31 are not necessarily isomorphic. Add a perfect matching between X30 and X31 such that no edges from M are included. Denote the resulting graph as X4. Define L4 as the set of all possible graphs X4 constructed via this method.

(5) To generate Lt, replicate the procedure in Step (4) using two graphs from Lt1, terminating at t=n1. For Ln1, select two graphs to form Xn10 and Xn11. Connect these via a perfect matching excluding edges from M¯, then append the matching M¯. The resultant n-dimensional structure is denoted as FHn=Xn10Xn11, FHn being composed of two (n1)-dimensional hypercube-like subgraphs (see Figure 3).

To facilitate understanding of the definition of the FHn network, we present the following algorithm for constructing the network. A similar algorithm can be provided for constructing an EFHn network. Therefore, we will not repeat the description here (see Algorithm 1).

Algorithm 1 ConstructFH (n)
Input: Integer n1.
Output:  FHn network structure.
1: function ConstructFH (n)
2:       if n=1 then;
3:             X1 Edge connecting nodes labeled 0 and 1;
4:             L1{X1};
5:             return L1;
6:         end if
7:         FHn1ConstructFH(n1);
8:         Xn10 ← Graph obtained by prefixing all node labels in FHn1 with 0;
9:         Xn11 ← Graph obtained by prefixing all node labels in FHn1 with 1;
10:         Add a perfect matching M connecting Xn10 and Xn11, where M does not
include edges from set M¯, and then append the matching M¯;
11:         FHn ← Graph formed by connecting Xn10 and Xn11 with perfect matchings M
and M¯;
12:         return FHn;
13: end function

Proposition 1

([26]). For n2,

(1) |V(FHn)|=2n and |E(FHn)|=(n+1)2n1;

(2) FHn is (n+1)-regular;

(3) g(FHn)3;

(4) |NFHn(x1)NFHn(x2)|2 for any x1,x2V(FHn);

(5) Each node of Xn10 has only two neighbors in Xn11, and similarly, each node of Xn11 also has only two neighbors in Xn10.

Definition 4

([27]). The n-dimensional EFHN EFHn can be defined recursively:

(1) Create two instances of FHn: Form FHn10 by prefixing labels with 0, and similarly derive FHn11 by prefixing labels with 1.

(2) Connect FHn10 and FHn11 with a perfect matching M¯, then add another perfect matching M between the two. The resulting graph is denoted as EFHn=FHn10FHn11 (see Figure 4).

Proposition 2

([27]). For n2,

(1) |V(EFHn)|=2n and |E(EFHn)|=(n+2)2n1;

(2) EFHn is (n+2)-regular;

(3) g(EFHn)3;

(4) |NEFHn(x1)NEFHn(x2)|2 for any x1,x2V(EFHn)(n5).

3. The 1-Extra Connectivity of EFHNs

Next, we will show the 1-extra connectivity of EFHNs. Before that, some well-known lemmas are first presented.

Lemma 1

([29]). Let Xn be an n-dimensional hypercube-like network. κ(Xn)=n for n2.

Lemma 2

([30]). For n3.

(1) κ1(Xn)=2n2;

(2) κ3(Xn)=2n2.

Lemma 3

([26]). κ(FHn)=λ(FHn)=n+1 for n2.

Lemma 4

([27]). κ(EFHn)=λ(EFHn)=n+2 for n2.

Lemma 5

([7]). Let m4, for any Xm, RV(Xm) with |R|my12(y1)(y+2)1, then XmR has a large component M containing at least |V(M)|2m|R|(y1), where 1ym3.

Lemma 6.

For g(EFHn)4, |NEFHn(H)|2n+2 for any HV(EFHn) with |H|=2.

Proof. 

Without loss of generality, let H={y1,y2}. According to whether there is an edge connecting y1 and y2, the following cases are presented.

Case 1. (y1,y2)E(FHn).

By Proposition 2, since |NEFHn(y1)NEFHn(y2)|2 and (y1,y2)E(EFHn),

|NEFHn(H)|=|NEFHn(y1)|+|NEFHn(y2)||NEFHn(y1)NEFHn(y2)|n+2+n+22=2n+2.

Case 2. (y1,y2)E(EFHn).

By Proposition 2, since (y1,y2)E(EFHn) and g(EFHn)4, |NEFHn(y1)NEFHn(y2)|=0. Thus,

|NEFHn(H)|=|NEFHn(y1)|+|NEFHn(y2)||NEFHn(y1)NEFHn(y2)||{y1,y2}|n+2+n+202=2n+2.

Lemma 7.

For g(FHn)4, n5, and HV(FHn) with |H|2n1, FHnH satisfies one of the following conditions:

(1) FHnH is connected.

(2) FHnH has exactly two components, one of which is an isolated node.

Proof. 

Since V(FHn)=V(Xn10)V(Xn11), let H0=V(Xn10)H and H1=V(Xn11)H. Thus, H=H0H1.

Case 1. Both Xn10H0 and Xn11H1 are connected.

Since Xn1iHi is connected and |V(Xn1i)|=2n1>2n1 for each i{0,1} and n5, Xn10H0 is connected to Xn11H1, namely, FHnH is connected, a contradiction.

Case 2. Both Xn10H0 and Xn11H1 are disconnected.

By Lemma 1, κ(Xn)=n. Since both Xn10H0 and Xn11H1 are disconnected, n1|H0|2n1 and n1|H1|2n1. Additionally, |H0|2n5 and |H1|2n5; otherwise, it will be in contradiction with n1|H0| and n1|H1| for n5. In word, n1|H0|2n5 and n1|H1|2n5. By Lemma 5, Xn10H0 (respectively Xn11H1) has only two components, one of which is an isolated node, say h0 (respectively h1) in Xn10H0 (respectively Xn11H1). Since V(Xn10)|H0||{h0}|>2n1, Xn10H0 is connected to Xn11H1. We say h0 or h1 is connected to FHnH{h0,h1}. By contrast, h0 and h1 are disconnected to FHnH{h0,h1}. Then, NFHn({h0,h1})H. That is, |H||NFHn({h0,h1})|2n, a contradiction. Therefore, there is at most one isolated node in FHnH.

Case 3. Xn1iHi is connected and Xn11iH1i is disconnected for i{0,1}.

Without loss of generality, let Xn10H0 be connected and Xn11H1 be disconnected. Then, 2n1|H1|n1 and |H0|n by Lemma 1.

Subcase 3.1. n1|H1|2n5.

By Lemma 5, Xn11H1 has a large connected component containing at least 2n1|H1|1 nodes for |H1|2n5. Therefore, when Xn11H1 is disconnected, Xn11H1 has a large connected component containing 2n1|H1| nodes and an isolated node, denoted by x. On the other hand, because Xn10H0 is connected and |V(Xn10)||H0||{x}|n1=2n1n1>2n1=|H|, Xn10H0 is disconnected to Xn11H1{x}. If the isolated node x is connected to Xn10H0, then FHnH is connected; otherwise, FHnH has only two components, one of which is an isolated node x.

Subcase 3.2. 2n4|H1|2n1.

Since 2n4|H1|2n1, |H0|2n1(2n4)=3. According to Definition 3, there exists at most one node in Xn11 whose neighbors in Xn10 are all located in H1. If this node exists in Xn11, then we denote it as v. Therefore, each node in V(Xn10){v} is connected to Xn10H0. In a word, FHnH is connected or FHnH has only two components, one of which is an isolated node v. □

Lemma 8.

For g(EFHn)4, n5, and HV(EFHn) with |H|2n+1, EFHnH satisfies one of the following conditions:

(1) EFHnH is connected.

(2) EFHnH has exactly two components, one of which is an isolated node.

Proof. 

Since V(EFHn)=V(FHn10)V(FHn11), we set H0=V(FHn10)H and H1=V(FHn11)H. Thus, H=H0H1. We discuss the following cases based on whether FHn1iHi is connected for i{0,1}.

Case 1. Both FHn10H0 and FHn11H1 are connected.

Let T0 be the connected component in FHn10H0, and T1 be the connected component in FHn11H1. Since FHn1iHi is connected and |V(FHn1i)|=2n1>2n+1 for each i{0,1} and n5, T0 is connected to T1, that is, EFHnH is connected.

Case 2. Both FHn10H0 and FHn11H1 are disconnected.

Since both FHn10H0 and FHn11H1 are disconnected, n|H0|2n+1 and n|H1|2n+1 by Lemma 3.

Subcase 2.1. |H0|=|H1|=n.

Since |H0|=|H1|=n, FHn10H0 (respectively, FHn11H1) has only two components, one of which is an isolated node, say h0 (respectively, h1) in FHn10H0 (respectively, FHn11H1) by Lemma 7. Let Ti be the component in FHn1iHi{hi} for i{0,1}. Therefore, |V(Ti)|=|V(FHn1i)||Hi||{hi}|=2n1n1>2n1|H| for each i{0,1}. Thus, T0 is connected to T1.

(1) If hi is connected to T1i, then EFHnH is connected for each i{0,1}.

(2) If hi is connected to T1i and h1i is disconnected to Ti and hi, then EFHnH has exactly two components, one of which is an isolated node h1i for each i{0,1} (see Figure 5a).

(3) If hi is connected to T1i, h1i is connected to Ti, and h1i is disconnected to hi, then EFHnH is connected for each i{0,1} (see Figure 5b).

(4) If hi is connected to T1i, h1i is connected to hi, and h1i is disconnected to Ti, then EFHnH is connected for each i{0,1} (see Figure 5c).

(5) If hi is disconnected to T1i for each i{0,1}, then NEFHn({h0,h1})H, that is, |NEFHn({h0,h1}||H|2n+1. However, |NEFHn({h0,h1}|2n+2 by Lemma 6. Hence, h0 is connected to T1, or h1 is connected to T0 (see Figure 5d).

Subcase 2.2. |H0|=n and |H1|=n+1.

Since |H0|=n2n3 and |H1|=n+12n3, FHn10H0 and FHn11H1 have only two components, one of which is an isolated node, say h0 and h1 in FHn10H0 and FHn11H1 by Lemma 7, respectively. Therefore, similar to the discussion in Subcase 2.1, it can be concluded that this lemma holds.

Subcase 2.3. |H0|=n+1 and |H1|=n.

Analogous to Subcase 2.2, we can deduce that this lemma is valid.

Case 3. FHn1iHi is connected, and FHn11iH1i is disconnected for i{0,1}.

Without loss of generality, let FHn10H0 be connected and FHn11H1 be disconnected. Since FHn11H1 is disconnected, n|H1|2n+1 and |H0|n+1.

Subcase 3.1. n|H1|2n3.

By Lemma 7, since FHn11H1 is disconnected, FHn1H1 has exactly two components, one of which is an isolated node, say h1 in FHn11H1. On the other hand, because |V(FHn10)||H0|2n1(n+1)>2n1|H|, FHn10H0 is connected to FHn11H1{h1}. In this case, if h1 is connected to FHn10H0, then EFHnH is connected; otherwise, EFHnH has exactly two components, one of which is an isolated node h1.

Subcase 3.2. 2n2|H1|2n+1.

Since 2n2|H1|2n+1, |H0||H||H1|2n1(2n4)=3. Therefore, FHn10H0 is connected. According to Definition 4, there exists at most one node in FHn11 whose neighbors in FHn10 are all located in H1. If this node exists in FHn11, then we denote it as y. Therefore, each node in V(FHn10){y} is connected to FHn10H0. In a word, EFHnH is connected or EFHnH has only two components, one of which is an isolated node y. □

Lemma 8 obtains the size of each component in the remaining graph of EFHn after deleting up to 2n+1 nodes. Building on the results of Lemma 8, we derive the following theorem.

Theorem 3.

For g(EFHn)4, n5, and HV(EFHn) with |H|2n+1, the 1-extra connectivity of EFHn is κ1(EFHn)=2n+2.

Proof. 

First, we show that κ1(EFHn)2n+2. Assume for contradiction that κ1(EFHn)2n+1. Let H be a minimum 1-extra cut of EFHn. Then |H|=κ1(EFHn)2n+1. By Lemma 8, EFHnH has a small component M with |M|1, a contradiction.

Next, we prove that κ1(EFHn)2n+2. Let (y1,y2)E(EFHn) and H=NEFHn({y1,y2}). Then, |H|=2n+2. Noting that |NEFHn[{y1,y2}]|=2n+4<|V(EFHn)|, we have V(EFHn){y1,y2}H, which implies that H is a node cut. Since g(EFHn)4, |NEFHn(c)NEFHn({y1,y2})|2 for any node cV(EFHn){y1,y2}H. Thus, we have dEFHnS(c)n+22=n5, then H is a 1-extra node cut. Therefore, κ1(EFHn)2n+2. □

4. The 1-ED of the EFHN Under the P-M and the M-M

In this section, we will show the 1-ED of the EFHN under the P-M and the M-M. Before that, some well-known lemmas are first presented.

Lemma 9

([31]). If |V(G)|2[κm(G)+m]+1 and there exists a subgraph P of G such that |V(P)|=m+1 and NG(V(P)) are the minimum m-extra-cut of G, then tmP(G)=κm(G)+h, where G is a n-regular and tmP(G) is the m-ED of G under the P-M for 0mn.

Combining Theorem 3 and Lemma 9, we have Theorem 4.

Theorem 4.

For g(EFHn)4 and n5, t1P(EFHn)=2n+3.

Lemma 10.

For g(EFHn)4 and n5, t1M(EFHn)2n+3.

Proof. 

We set H1=NEFHn({y1,y2}) and H2=NEFHn[{y1,y2}] with (y1,y2)E(EFHn). Thus, |H1|=|NEFHn({y1,y2})|=2n+2 and |H2|=|NEFHn[{y1,y2}]|=2n+4.

Since H1=NEFHn({y1,y2}) and H2=NEFHn[{y1,y2}], H1ΔH2={y1,y2}. Moreover, there is no edge between H1ΔH2={y1,y2} and V(EFHn)(H1H2). By Lemma 2, H1 and H2 are indistinguishable under the M-M. Thus, EFHn is not 1-extra (2n+4)-diagnosable by Definition 2. Hence, t1M(EFHn)2n+3. □

Lemma 11.

For g(EFHn)4 and n6, t1M(EFHn)2n+3.

Proof. 

On the contrary, assume that t1M(EFHn)<2n+3. Then, in EFHn, there exist two different 1-extra faulty subsets H1 and H2. Both |H1|,|H2|2n+3, yet H1 and H2 cannot be distinguished. Given that |V(EFHn)|=2n, it follows that V(EFHn) is not equal to the union of H1 and H2.

Next, we assert that V(EFHn)H1H2 contains no isolated nodes. For the sake of contradiction, assume there is at least one isolated node in V(EFHn)H1H2. Let W denote the set of all isolated nodes within V(EFHn)H1H2. Take an arbitrary node xW. First, we demonstrate that H1H2 and H2H1. If H1H2=, then H1H2. Given that H2 is a 1-extra faulty set, the existence of an isolated node x leads to a contradiction. Hence, H1H2. By similar reasoning, H2H1. Next, we show that |NEFHn(x)(H1H2)|=|NEFHn(x)(H2H1)|=1. Suppose there are two nodes, say u,vH1H2, such that (u,x)E(EFHn) and (v,x)E(EFHn). By Lemma 2, H1 and H2 would be distinguishable, a contradiction. If no node uH1H2 satisfies (u,x)E(EFHn), then NEFHn(x)H2. Since H2 is a 1-extra faulty set, this implies x is an isolated node, another contradiction. Thus, |NEFHn(x)(H1H2)|=1, and similarly, |NEFHn(x)(H2H1)|=1. Consequently, for any xW, |NEFHn(x)(H1H2)|=n. Thus, |H1H2|n and

xW|NEFHn[(H1H2)W](W)||W|nzH1H2dEFHn(z)|H1H2|(n+2)(|H2|1)(n+2)(2n+2)(n+2).

Then, |W|(2n+2)(n+2)n(2n+2)2nn4n+4. Let H=V(EFHn)(H1H2)W. Thus,

|H|=|V(EFHn)|+|H1H2||H1||H2||W|2n+n2(2n+3)4n4>0,

which means that H.

Considering that H contains no isolated nodes, and by the assumption that H1 and H2 are indistinguishable, there are no edges between H and H1ΔH2. Additionally, since W is the set of isolated nodes in V(EFHn)(H1H2), no edges exist between H and W. Consequently, EFHn(H1H2) is disconnected, implying that H1H2 is a node cut. Noting that H1 and H2 are distinct 1-extra faulty subsets, every component of EFHnH1 and EFHnH2 contains at least two nodes. Thus, H1H2 is a 1-extra node cut. By Theorem 3, we have |H1H2|2n+2. Given |H1|2n+3 and |H2|2n+3, it follows that |H1H2|=|H2H1|=1. Let H1H2={c} and H2H1={d}. Since |NEFHn(c)NEFHn(d)|2, we conclude |W|2.

When |W|=1, let W={x}. Since V(H)NEFHn({c,d,x})=, it follows that NEFHn({c,d,x})H1H2 (see Figure 2). By the principle of inclusion–exclusion, |H1H2||NEFHn({c,d,x})|=(|NEFHn({c})|+|NEFHn({d})|+|NEFHn({x})|)(|NEFHn({c})NEFHn({d})|+|NEFHn({c})NEFHn({x})|+|NEFHn({d})NEFHn({x})|)+|NEFHn({c})NEFHn({d})NEFHn({x})||{c,d,x}|=3(n+2)(2+0+0)+03=3n+1. Thus, |H2|=|H2H1|+|H1H2|1+3n+1=3n+2>2n+3|H1|, which leads to a contradiction.

When |W|=2, let W={x,y} where xy. According to Proposition 2, the sets NEFHn(c){x,y}, NEFHn(d){x,y}, NEFHn(x){c,d}, and NEFHn(y){c,d} are pairwise disjoint. Consequently, |H1H2|NEFHn(c){x,y}+NEFHn(d){x,y}+NEFHn(x){c,d}+NEFHn(y){c,d}=n+n+n+n=4n. Thus, |H2|=|H2H1|+|H1H2|1+4n>2n+3|H1|, which yields a contradiction.

Thus, for any node bEFHn(H1H2), there is at least one node b such that (b,b)E(EFHn). As H1 and H2 are indistinguishable, no edges exist between H1ΔH2 and EFHn(H1H2), meaning H1H2 is a node cut. Moreover, since H1 and H2 are distinct 1-extra faulty sets, H1H2 forms a 1-extra node cut. By Theorem 3, this implies |H1H2|2 and |H1H2|2n+2. Consequently,

|H1|=|H1H2|+|H1H2|2+2n+2>2n+3|H1|,

leading to a contradiction.

In conclusion, t1M(EFHn)2n+3 holds. □

By Lemmas 10 and 11, Theorem 5 is obtained.

Theorem 5.

For g(EFHn)4 for n6, t1M(EFHn)=2n+3.

5. Conclusions

In this paper, we study the 1-extra connectivity as well as the 1-ED of the n-dimensional EFHN. Specifically, we prove that the 1-extra connectivity of this network is 2n+2. Additionally, for the n-dimensional EFHN (with n>5), the 1-ED under both the P-M and the M-M is determined to be 2n+3. These findings, as presented above, clearly demonstrate that for a relatively large value of n, both the 1-extra connectivity and the 1-ED are nearly double the value of the traditional diagnosability.

Author Contributions

Conceptualization, Y.W. and C.-K.L.; methodology, C.-K.L.; validation Y.W. and C.-K.L.; writing—original draft preparation, Y.W.; writing—review and editing, C.-K.L.; supervision, C.-K.L.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

No new data were created or analyzed in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures

Figure 1 Illustration of distinguishability under the P-M.

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Figure 3 FH4.

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Figure 4 EFH4.

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Figure 5 Illustration of Subcase 2.1 of Lemma 8 (ad).

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