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.     

Finding a dominating set on bipartite graphs

Finding a dominating set of minimum cardinality is an NP-hard graph problem, even when the graph is bipartite. In this paper we are interested in solving the problem on graphs having a large independent set. Given a graph G with an independent set of size z, we show that the problem can be solved in time O^∗(²n−z), where n is the number of vertices of G. As a consequence, our algorithm is able to solve the dominating set problem on bipartite graphs in time O^∗(²n/2). Another implication is an algorithm for general graphs whose running time is O(n^1.7088).     

Fast 3-coloring Triangle-Free Planar Graphs

Although deciding whether the vertices of a planar graph can be colored with three colors is NP-hard, the widely known Grötzsch’s theorem states that every triangle-free planar graph is 3-colorable. We show the first o(n ²) algorithm for 3-coloring vertices of triangle-free planar graphs. The time complexity of the algorithm is $\mathcal{O}(n\log n)Although deciding whether the vertices of a planar graph can be colored with three colors is NP-hard, the widely known Gr?tzsch’s theorem states that every triangle-free planar graph is 3-colorable. We show the first o(n ²) algorithm for 3-coloring vertices of triangle-free planar graphs. The time complexity of the algorithm is O(nlogn)\mathcal{O}(n\log n) .     

Maximum Matching in Regular and Almost Regular Graphs

We present an O(n ²logn)-time algorithm that finds a maximum matching in a regular graph with n vertices. More generally, the algorithm runs in O(rn ²logn) time if the difference between the maximum degree and the minimum degree is less than r. This running time is faster than applying the fastest known general matching algorithm that runs in $O(\sqrt{n}m)$ -time for graphs with m edges, whenever m=ω(rn ^1.5logn).     

Minimum cycle bases of weighted outerplanar graphs

We give the first optimal algorithm that computes a minimum cycle basis for any weighted outerplanar graph. Specifically, for any n-node edge-weighted outerplanar graph G, we give an O(n)-time algorithm to obtain an O(n)-space compact representation Z(C) for a minimum cycle basis C of G. Each cycle in C can be computed from Z(C) in O(1) time per edge. Our result works for directed and undirected outerplanar graphs G.     

Intractability of min- and max-cut in streaming graphs

We show that the exact computation of a minimum or a maximum cut of a given graph G is out of reach for any one-pass streaming algorithm, that is, for any algorithm that runs over the input stream of G's edges only once and has a working memory of o(n²) bits. This holds even if randomization is allowed.     

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).     

A new analysis of a self-stabilizing maximum weight matching algorithm with approximation ratio 2

The maximum weight matching problem is a fundamental problem in graph theory with a variety of important applications. Recently Manne and Mjelde presented the first self-stabilizing algorithm computing a 2-approximation of the optimal solution. They established that their algorithm stabilizes after O(2ⁿ) (resp. O(3ⁿ)) moves under a central (resp. distributed) scheduler. This paper contributes a new analysis, improving these bounds considerably. In particular it is shown that the algorithm stabilizes after O(nm) moves under the central scheduler and that a modified version of the algorithm also stabilizes after O(nm) moves under the distributed scheduler. The paper presents a new proof technique based on graph reduction for analyzing the complexity of self-stabilizing algorithms.     

Optimal per-edge processing times in the semi-streaming model

We present semi-streaming algorithms for basic graph problems that have optimal per-edge processing times and therefore surpass all previous semi-streaming algorithms for these tasks. The semi-streaming model, which is appropriate when dealing with massive graphs, forbids random access to the input and restricts the memory to bits.Particularly, the formerly best per-edge processing times for finding the connected components and a bipartition are O(α(n)), for determining k-vertex and k-edge connectivity O(k²n) and O(n⋅logn) respectively for any constant k and for computing a minimum spanning forest O(logn). All these time bounds we reduce to O(1).Every presented algorithm determines a solution asymptotically as fast as the best corresponding algorithm up to date in the classical RAM model, which therefore cannot convert the advantage of unlimited memory and random access into superior computing times for these problems.     

Parameterized algorithm for eternal vertex cover

In this paper we initiate the study of a “dynamic” variant of the classical Vertex Cover problem, the Eternal Vertex Cover problem introduced by Klostermeyer and Mynhardt, from the perspective of parameterized algorithms. This problem consists in placing a minimum number of guards on the vertices of a graph such that these guards can protect the graph from any sequence of attacks on its edges. In response to an attack, each guard is allowed either to stay in his vertex, or to move to a neighboring vertex. However, at least one guard has to fix the attacked edge by moving along it. The other guards may move to reconfigure and prepare for the next attack. Thus at every step the vertices occupied by guards form a vertex cover. We show that the problem admits a kernel of size k⁴(k+1)+2k, which shows that the problem is fixed parameter tractable when parameterized by the number of available guards k. Finally, we also provide an algorithm with running time O(²O(k²)+nm) for Eternal Vertex Cover, where n is the number of vertices and m the number of edges of the input graph. In passing we also observe that Eternal Vertex Cover is NP-hard, yet it has a polynomial time 2-approximation algorithm.     

Improved fixed parameter tractable algorithms for two “edge” problems: MAXCUT and MAXDAG

We give improved parameterized algorithms for two “edge” problems MAXCUT and MAXDAG, where the solution sought is a subset of edges. MAXCUT of a graph is a maximum set of edges forming a bipartite subgraph of the given graph. On the other hand, MAXDAG of a directed graph is a set of arcs of maximum size such that the graph induced on these arcs is acyclic. Our algorithms are obtained through new kernelization and efficient exact algorithms for the optimization versions of the problems. More precisely our results include:

(i)

a kernel with at most αk vertices and βk edges for MAXCUT. Here 0<α?1 and 1<β?2. Values of α and β depends on the number of vertices and the edges in the graph;

(ii)

a kernel with at most 4k/3 vertices and 2k edges for MAXDAG;

(iii)

an O^∗(k^1.2418) parameterized algorithm for MAXCUT in undirected graphs. This improves the O^∗(k^1.4143)¹ algorithm presented in [E. Prieto, The method of extremal structure on the k-maximum cut problem, in: The Proceedings of Computing: The Australasian Theory Symposium (CATS), 2005, pp. 119-126];

(iv)

an O^∗(n²) algorithm for optimization version of MAXDAG in directed graphs. This is the first such algorithm to the best of our knowledge;

(v)

an O^∗(k²) parameterized algorithm for MAXDAG in directed graphs. This improves the previous best of O^∗(k⁴) presented in [V. Raman, S. Saurabh, Parameterized algorithms for feedback set problems and their duals in tournaments, Theoretical Computer Science 351 (3) (2006) 446-458];

(vi)

an O^∗(k¹⁶) parameterized algorithm to determine whether an oriented graph having m arcs has an acyclic subgraph with at least m/2+k arcs. This improves the O^∗(k²) algorithm given in [V. Raman, S. Saurabh, Parameterized algorithms for feedback set problems and their duals in tournaments, Theoretical Computer Science 351 (3) (2006) 446-458].

In addition, we show that if a directed graph has minimum out degree at least f(n) (some function of n) then Directed Feedback Arc Set problem is fixed parameter tractable. The parameterized complexity of Directed Feedback Arc Set is a well-known open problem.     

Improved algorithm for all pairs shortest paths

We present an improved algorithm for all pairs shortest paths. For a graph of n vertices our algorithm runs in O(n³(loglogn/logn)^5/7) time. This improves the best previous result which runs in O(n³(loglogn/logn)^1/2) time.     

A simple algorithm to find Hamiltonian cycles in proper interval graphs

We present an algorithm to find a Hamiltonian cycle in a proper interval graph in O(m+n) time, where m is the number of edges and n is the number of vertices in the graph. The algorithm is simpler and shorter than previous algorithms for the problem.     

Efficient Graph-Theoretic Algorithms on a Linear Array with a Reconfigurable Pipelined Bus System

We present efficient algorithms for solving several fundamental graph-theoretic problems on a Linear Array with a Reconfigurable Pipelined Bus System (LARPBS), one of the recently proposed models of computation based on optical buses. Our algorithms include finding connected components, minimum spanning forest, biconnected components, bridges and articulation points for an undirected graph. We compute the connected components and minimum spanning forest of a graph in O(log n) time using O(m+n) processors where m and n are the number of edges and vertices in the graph and m=O(n ²) for a dense graph. Both the processor and time complexities of these two algorithms match the complexities of algorithms on the Arbitrary and Priority CRCW PRAM models which are two of the strongest PRAM models. The algorithms for these two problems published by Li et al. [7] have been considered to be the most efficient on the LARPBS model till now. Their algorithm [7] for these two problems require O(log n) time and O(n ³/log n) processors. Hence, our algorithms have the same time complexity but require less processors. Our algorithms for computing biconnected components, bridges and articulation points of a graph run in O(log n) time on an LARPBS with O(n ²) processors. No previous algorithm was known for these latter problems on the LARPBS.     

An improved exact algorithm for the domatic number problem

The 3-domatic number problem asks whether a given graph can be partitioned into three dominating sets. We prove that this problem can be solved by a deterministic algorithm in time n^2.695 (up to polynomial factors) and in polynomial space. This result improves the previous bound of n^2.8805, which is due to Björklund and Husfeldt. To prove our result, we combine an algorithm by Fomin et al. with Yamamoto's algorithm for the satisfiability problem. In addition, we show that the 3-domatic number problem can be solved for graphs G with bounded maximum degree Δ(G) by a randomized polynomial-space algorithm, whose running time is better than the previous bound due to Riege and Rothe whenever Δ(G)?5. Our new randomized algorithm employs Schöning's approach to constraint satisfaction problems.     

All-pairs shortest-paths computation in the presence of negative cycles

We present an algorithm that solves the all-pairs shortest-paths problem on a directed graph with n vertices and m arcs in time O(nm+n²logn), where the arcs are assigned real, possibly negative costs. Our algorithm is new in the following respect. It computes the distance μ(v,w) between each pair v,w of vertices even in the presence of negative cycles, where μ(v,w) is defined as the infimum of the costs of all directed paths from v to w.     

Improved edge-coloring with three colors

We show an O(1.344ⁿ)=O(2^0.427n) algorithm for edge-coloring an n-vertex graph using three colors. Our algorithm uses polynomial space. This improves over the previous O(2^n/2) algorithm of Beigel and Eppstein [R. Beigel, D. Eppstein, 3-coloring in time O(1.3289n), J. Algorithms 54 (2) (2005) 168–204.]. We apply a very natural approach of generating inclusion–maximal matchings of the graph. The time complexity of our algorithm is estimated using the “measure and conquer” technique.     

Boolean-width of graphs

We introduce the graph parameter boolean-width, related to the number of different unions of neighborhoods-Boolean sums of neighborhoods-across a cut of a graph. For many graph problems, this number is the runtime bottleneck when using a divide-and-conquer approach. For an n-vertex graph given with a decomposition tree of boolean-width k, we solve Maximum Weight Independent Set in time O(n²k2^2k) and Minimum Weight Dominating Set in time O(n²+nk2^3k). With an additional n² factor in the runtime, we can also count all independent sets and dominating sets of each cardinality.Boolean-width is bounded on the same classes of graphs as clique-width. boolean-width is similar to rank-width, which is related to the number of GF(2)-sums of neighborhoods instead of the Boolean sums used for boolean-width. We show for any graph that its boolean-width is at most its clique-width and at most quadratic in its rank-width. We exhibit a class of graphs, the Hsu-grids, having the property that a Hsu-grid on Θ(n²) vertices has boolean-width Θ(logn) and rank-width, clique-width, tree-width, and branch-width Θ(n).     

An Optimal Shortest Path Parallel Algorithm for Permutation Graphs

We present an optimal parallel algorithm for the single-source shortest path problem for permutation graphs. The algorithm runs in O(log n) time using O(n/log n) processors on an EREW PRAM. As an application, we show that a minimum connected dominating set in a permutation graph can be found in O(log n) time using O(n/log n) processors.     

Computing rank-width exactly

We prove that the rank-width of an n-vertex graph can be computed exactly in time O(n²n³log²nloglogn). To improve over a trivial O(n³logn)-time algorithm, we develop a general framework for decompositions on which an optimal decomposition can be computed efficiently. This framework may be used for other width parameters, including the branch-width of matroids and the carving-width of graphs.