A self-stabilizing algorithm for constructing weakly connected minimal dominating sets

This paper presents a new distributed self-stabilizing algorithm for the weakly connected minimal dominating set problem. It assumes a self-stabilizing algorithm to compute a breadth-first tree. Using an unfair distributed scheduler the algorithm stabilizes in at most O(nmA) moves, where A is the number of moves to construct a breadth-first tree. All previously known algorithms required an exponential number of moves.     

Linear self-stabilizing algorithms for the independent and dominating set problems using an unfair distributed scheduler

This paper presents distributed self-stabilizing algorithms for the maximal independent and the minimal dominating set problems. Using an unfair distributed scheduler the algorithms stabilizes in at most max{3n−5,2n} resp. 9n moves. All previously known algorithms required O(n²) moves.     

Efficient transformation of distance-2 self-stabilizing algorithms

Self-stabilizing algorithms for optimization problems can often be solved more easily using the distance-two model in which each vertex can instantly see the state information of all vertices up to distance two. This paper presents a new technique to emulate algorithms for the distance-two model on the distance-one model using the distributed scheduler with a slowdown factor of O(m)$O\left(m\right)$ moves. Up until now the best transformer had a slowdown factor of O(n²m)$O\left(n^{2}m\right)$ moves. The technique is used to derive improved self-stabilizing algorithms for several graph domination problems. The paper also introduces a generalization of the distance-two model allowing a more space efficient transformer.     

Uniform and Self-Stabilizing Fair Mutual Exclusion on Unidirectional Rings under Unfair Distributed Daemon

This paper presents a uniform randomized self-stabilizing mutual exclusion algorithm for an anonymous unidirectional ring of any size n, running under an unfair distributed scheduler (d-daemon). The system is stabilized with probability 1 in O(n³) expected number of steps, and each process is privileged at least once in every 2n steps, once it is stabilized.     

Dominating set based exact algorithms for 3-coloring

We show that the 3-colorability problem can be solved in O(n^1.296) time on any n-vertex graph with minimum degree at least 15. This algorithm is obtained by constructing a dominating set of the graph greedily, enumerating all possible 3-colorings of the dominating set, and then solving the resulting 2-list coloring instances in polynomial time. We also show that a 3-coloring can be obtained in ²o(n) time for graphs having minimum degree at least ω(n) where ω(n) is any function which goes to ∞. We also show that if the lower bound on minimum degree is replaced by a constant (however large it may be), then neither a ²o(n) time nor a ²o(m) time algorithm is possible (m denotes the number of edges) for 3-colorability unless Exponential Time Hypothesis (ETH) fails. We also describe an algorithm which obtains a 4-coloring of a 3-colorable graph in O(n^1.2535) time.     

A self-stabilizing algorithm to maximal 2-packing with improved complexity

In self-stabilization, each node has a local view of the distributed network system, in a finite amount of time the system converges to a global setup with desired property, in this case establishing a 2-packing set. Using a graph G=(V,E)$G=(V,E)$ to represent the network, a subset S⊆V$S\subseteq V$ is a 2-packing if ∀i∈V:|N[i]∩S|?1$\forall i\in V:|N[i]\cap S|?1$. In this paper, we first propose an ID-based, constant space, self-stabilizing algorithm that stabilizes to a maximal 2-packing in an arbitrary graph. We show that the algorithm stabilizes in O(mn)$O(mn)$ moves under any scheduler (such as a distributed daemon). Secondly, we show that the algorithm stabilizes in O(n²)$O(n^2)$ rounds under a synchronous daemon where every privileged node moves at each round.     

Self-stabilizing leader election in optimal space under an arbitrary scheduler

A silent self-stabilizing asynchronous distributed algorithm, SSLE, is given for the leader election problem in a connected unoriented (bidirectional) network with unique IDs. SSLE also constructs a BFS tree on the network rooted at that leader. SSLE uses O(logn) space per process and stabilizes in O(n) rounds, against the unfair daemon, where n is the number of processes in the network.     

Fully dynamic biconnectivity in graphs

We present an algorithm for maintaining the biconnected components of a graph during a sequence of edge insertions and deletions. It requires linear storage and preprocessing time. The amortized running time for insertions and for deletions isO(m ^2/3 ), wherem is the number of edges in the graph. Any query of the form ‘Are the verticesu andv biconnected?’ can be answered in timeO(1). This is the first sublinear algorithm for this problem. We can also output all articulation points separating any two vertices efficiently. If the input is a plane graph, the amortized running time for insertions and deletions drops toO(√n logn) and the query time isO(log² n), wheren is the number of vertices in the graph. The best previously known solution takes timeO(n ^2/3 ) per update or query.     

A new self-stabilizing maximal matching algorithm

The maximal matching problem has received considerable attention in the self-stabilizing community. Previous work has given several self-stabilizing algorithms that solve the problem for both the adversarial and the fair distributed daemon, the sequential adversarial daemon, as well as the synchronous daemon. In the following we present a single self-stabilizing algorithm for this problem that unites all of these algorithms in that it has the same time complexity as the previous best algorithms for the sequential adversarial, the distributed fair, and the synchronous daemon. In addition, the algorithm improves the previous best time complexities for the distributed adversarial daemon from O(n²)$O\left(n^2\right)$ and O(δm)$O\left(\delta m\right)$ to O(m)$O\left(m\right)$ where n$n$ is the number of processes, m$m$ is the number of edges, and δ$\delta$ is the maximum degree in the graph.     

An efficient distributed algorithm for maximum matching in general graphs

We present a distributed algorithm for maximum cardinality matching in general graphs. On a general graph withn vertices, our algorithm requiresO(n ^5/2) messages in the worst case. On trees, our algorithm computes a maximum matching usingO(n) messages after the election of a leader.Work on this paper has been supported by the Office of Naval Research under Contract N00014-85-K-0570.     

Linear time self-stabilizing colorings

We propose two new self-stabilizing distributed algorithms for proper Δ+1 (Δ is the maximum degree of a node in the graph) colorings of arbitrary system graphs. Both algorithms are capable of working with multiple type of daemons (schedulers) as is the most recent algorithm by Gradinariu and Tixeuil [OPODIS'2000, 2000, pp. 55-70]. The first algorithm converges in O(m) moves while the second converges in at most n moves (n is the number of nodes and m is the number of edges in the graph) as opposed to the O(Δ×n) moves required by the algorithm by Gradinariu and Tixeuil [OPODIS'2000, 2000, pp. 55-70]. The second improvement is that neither of the proposed algorithms requires each node to have knowledge of Δ, as is required by Gradinariu and Tixeuil [OPODIS'2000, 2000, pp. 55-70]. Further, the coloring produced by our first algorithm provides an interesting type of coloring, called a Grundy Coloring [Jensen and Toft, Graph Coloring Problems, 1995].     

A 4n-move self-stabilizing algorithm for the minimal dominating set problem using an unfair distributed daemon

A distributed system is self-stabilizing if, regardless of its initial state, the system is guaranteed to reach a legitimate (i.e., correct) state in finite time. In 2007, Turau proposed the first linear-time self-stabilizing algorithm for the minimal dominating set (MDS) problem under an unfair distributed daemon [9]; this algorithm stabilizes in at most 9n moves, where n is the number of nodes in the system. In 2008, Goddard et al. [4] proposed a 5n-move algorithm. In this paper, we present a 4n-move algorithm.     

Distributed edge coloration for bipartite networks

This paper presents a self-stabilizing algorithm to color the edges of a bipartite network such that any two adjacent edges receive distinct colors. The algorithm has the self-stabilizing property; it works without initializing the system. It also works in a de-centralized way without a leader computing a proper coloring for the whole system. Moreover, it finds an optimal edge coloring and its time complexity is O(n ² k + m) moves, where k is the number of edges that are not properly colored in the initial configuration. This is a completely revised and extended version of [15]. This research was supported in part by the National Science Council of the Republic of China under the Contract NSC94-2213-E008-001.     

An efficient distributed algorithm for finding all hinge vertices in networks

Let G=(V, E) be a graph with vertex set V of size n and edge set E of size m. A vertex v∈V is called a hinge vertex if there exist two vertices in V\{v} such that their distance becomes longer when v is removed. In this paper, we present a distributed algorithm that finds all hinge vertices on an arbitrary graph. The proposed algorithm works for named static asynchronous networks and achieves O(n ²) time complexity and O(m) message complexity. In particular, the total messages exchanged during the algorithm are at most 2m(log n+nlog n+1) bits.     

An anonymous self-stabilizing algorithm for 1-maximal independent set in trees

We present an anonymous, constant-space, self-stabilizing algorithm for finding a 1-maximal independent set in tree graphs (and some rings). We show that the algorithm converges in O(n²) moves under any central daemon (one that at each time-step selects one of the privileged nodes to move).     

An efficient certifying algorithm for the Hamiltonian cycle problem on circular-arc graphs

A certifying algorithm for a problem is an algorithm that provides a certificate with each answer that it produces. The certificate is an evidence that can be used to authenticate the correctness of the answer. A Hamiltonian cycle in a graph is a simple cycle in which each vertex of the graph appears exactly once. The Hamiltonian cycle problem is to determine whether or not a graph contains a Hamiltonian cycle. The best result for the Hamiltonian cycle problem on circular-arc graphs is an O(n²logn)-time algorithm, where n is the number of vertices of the input graph. In fact, the O(n²logn)-time algorithm can be modified as a certifying algorithm although it was published before the term certifying algorithms appeared in the literature. However, whether there exists an algorithm whose time complexity is better than O(n²logn) for solving the Hamiltonian cycle problem on circular-arc graphs has been opened for two decades. In this paper, we present an O(Δn)-time certifying algorithm to solve this problem, where Δ represents the maximum degree of the input graph. The certificates provided by our algorithm can be authenticated in O(n) time.     

Relative neighborhood graphs in the Li-metric

The relative neighborhood graph of a set of n points in the plane under the L₁-metric is considered. An algorithm that runs in O(nlog n) time for constructing the relative neighborhood graph based on the Delaunay triangulation is presented, improving a previously known algorithm that runs in O(n²log n) time.     

Improved algorithm for finding next-to-shortest paths

We study the problem of finding the next-to-shortest paths in a weighted undirected graph. A next-to-shortest (u,v)-path is a shortest (u,v)-path amongst (u,v)-paths with length strictly greater than the length of the shortest (u,v)-path. The first polynomial algorithm for this problem was presented in [I. Krasikov, S.D. Noble, Finding next-to-shortest paths in a graph, Inform. Process. Lett. 92 (2004) 117-119]. We improve the upper bound from O(n³m) to O(n³).