Subexponential algorithms for partial cover problems

Partial Cover problems are optimization versions of fundamental and well-studied problems like Vertex Cover and Dominating Set. Here one is interested in covering (or dominating) the maximum number of edges (or vertices) using a given number k of vertices, rather than covering all edges (or vertices). In general graphs, these problems are hard for parameterized complexity classes when parameterized by k. It was recently shown by Amini et al. (2008) [1] that Partial Vertex Cover and Partial Dominating Set are fixed parameter tractable on large classes of sparse graphs, namely H-minor-free graphs, which include planar graphs and graphs of bounded genus. In particular, it was shown that on planar graphs both problems can be solved in time ²O(k)nO(1).During the last decade there has been an extensive study on parameterized subexponential algorithms. In particular, it was shown that the classical Vertex Cover and Dominating Set problems can be solved in subexponential time on H-minor-free graphs. The techniques developed to obtain subexponential algorithms for classical problems do not apply to partial cover problems. It was left as an open problem by Amini et al. (2008) [1] whether there is a subexponential algorithm for Partial Vertex Cover and Partial Dominating Set. In this paper, we answer the question affirmatively by solving both problems in time not only on planar graphs but also on much larger classes of graphs, namely, apex-minor-free graphs. Compared to previously known algorithms for these problems our algorithms are significantly faster and simpler.     

A new polynomial-time algorithm for the maximum weighted (χ(G) — 1)-coloring problem in comparability graphs

For a weighted graph G = (V, E), the maximum weightedk-coloring problem is to color a set of vertices of maximum weight usingk colors. In this paper we are interested in solving this problem in comparability graphs. For these graphs, as shown by Cameron, the problem can be translated into a dual transportation problem. Let (G) denote the chromatic number of a comparability graphG. We prove that whenk is equal to (G) — 1, the problem can be solved more efficiently by finding a maximum weighted stable set in a bipartite graph. Maximum matching algorithms can be used in the unweighted case.This work was supported by an NSERC International Fellowship to the University of Montréal, and by NSERC #OGP0105384.     

Trading uninitialized space for time

The design of efficient graph algorithms usually precludes the test of edge existence, because an efficient support of that operation already requires time for the initialization of an adjacency-matrix representation. We describe an alternative representation of static directed graphs taking Θ(n+m) initialization time and using Θ(n²) space, which supports the efficient implementation of all usual operations on static graphs. The sparse graph representation allows the design of efficient graph algorithms using both iteration over all vertices adjacent with a given vertex and edge-existence operations, although at the expense of additional (uninitialized) space which may, nevertheless, be used for other purposes. To the best of our knowledge, the representation leads to the first graph algorithms with the disconcerting property that the time complexity is better than the space complexity.     

Computational study on planar dominating set problem

Recently, there has been significant theoretical progress towards fixed-parameter algorithms for the DOMINATING SET problem of planar graphs. It is known that the problem on a planar graph with n vertices and dominating number k can be solved in time using tree/branch-decomposition based algorithms. In this paper, we report computational results of Fomin and Thilikos algorithm which uses the branch-decomposition based approach. The computational results show that the algorithm can solve the DOMINATING SET problem of large planar graphs in a practical time and memory space for the class of graphs with small branchwidth. For the class of graphs with large branchwidth, the size of instances that can be solved by the algorithm in practice is limited to about one thousand edges due to a memory space bottleneck. The practical performances of the algorithm coincide with the theoretical analysis of the algorithm. The results of this paper suggest that the branch-decomposition based algorithms can be practical for some applications on planar graphs.     

Heuristics for rapidly four-coloring large planar graphs

We present several algorithms for rapidly four-coloring large planar graphs and discuss the results of extensive experimentation with over 140 graphs from two distinct classes of randomly generated instances having up to 128,000 vertices. Although the algorithms can potentially require exponential time, the observed running times of our more sophisticated algorithms are linear in the number of vertices over the range of sizes tested. The use of Kempe chaining and backtracking together with a fast heuristic which usually, but not always, resolves impasses gives us hybrid algorithms that: (1) successfully four-color all our test graphs, and (2) in practice run, on average, only twice as slow as the well-known, nonexact, simple to code, Θ(n) saturation algorithm of Brélaz.     

Optimal algorithms for the single and multiple vertex updating problems of a minimum spanning tree

The vertex updating problem for a minimum spanning tree (MST) is defined as follows: Given a graphG=(V, E _G) and an MSTT forG, find a new MST forG to which a new vertexz has been added along with weighted edges that connectz with the vertices ofG. We present a set of rules that produce simple optimal parallel algorithms that run inO(lgn) time usingn/lgn EREW PRAM processors, wheren=¦V¦. These algorithms employ any valid tree-contraction schedule that can be produced within the stated resource bounds. These rules can also be used to derive simple linear-time sequential algorithms for the same problem. The previously best-known parallel result was a rather complicated algorithm that usedn processors in the more powerful CREW PRAM model. Furthermore, we show how our solution can be used to solve the multiple vertex updating problem: Update a given MST whenk new vertices are introduced simultaneously. This problem is solved inO(lgk·lgn) parallel time using (k·n)/(lgk·lgn) EREW PRAM processors. This is optimal for graphs having (kn) edges.Part of this work was done while P. Metaxas was with the Department of Mathematics and Computer Science, Dartmouth College.     

Exact Algorithms for Graph Homomorphisms

Graph homomorphism, also called H-coloring, is a natural generalization of graph coloring: There is a homomorphism from a graph G to a complete graph on k vertices if and only if G is k-colorable. During recent years the topic of exact (exponential-time) algorithms for NP-hard problems in general, and for graph coloring in particular, has led to extensive research. Consequently, it is natural to ask how the techniques developed for exact graph coloring algorithms can be extended to graph homomorphisms. By the celebrated result of Hell and Nesetril, for each fixed simple graph H, deciding whether a given simple graph G has a homomorphism to H is polynomial-time solvable if H is a bipartite graph, and NP-complete otherwise. The case where H is the cycle of length 5, is the first NP-hard case different from graph coloring. We show that for an odd integer , whether an input graph G with n vertices is homomorphic to the cycle of length k, can be decided in time . We extend the results obtained for cycles, which are graphs of treewidth two, to graphs of bounded treewidth as follows: if H is of treewidth at most t, then whether input graph G with n vertices is homomorphic to H can be decided in time .     

Quantum Algorithms for Matching Problems

We present quantum algorithms for the following matching problems in unweighted and weighted graphs with n vertices and m edges:

Incremental <Emphasis Type="Italic">k</Emphasis>-core decomposition: algorithms and evaluation

A k-core of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. k-core decomposition is often used in large-scale network analysis, such as community detection, protein function prediction, visualization, and solving NP-hard problems on real networks efficiently, like maximal clique finding. In many real-world applications, networks change over time. As a result, it is essential to develop efficient incremental algorithms for dynamic graph data. In this paper, we propose a suite of incremental k-core decomposition algorithms for dynamic graph data. These algorithms locate a small subgraph that is guaranteed to contain the list of vertices whose maximum k-core values have changed and efficiently process this subgraph to update the k-core decomposition. We present incremental algorithms for both insertion and deletion operations, and propose auxiliary vertex state maintenance techniques that can further accelerate these operations. Our results show a significant reduction in runtime compared to non-incremental alternatives. We illustrate the efficiency of our algorithms on different types of real and synthetic graphs, at varying scales. For a graph of 16 million vertices, we observe relative throughputs reaching a million times, relative to the non-incremental algorithms.     

Minimized Embedding of Arbitrary Hamiltonian Graphs in Fault-tolerant Graph and Reconfiguration at Faults. I. One-fault-tolerant Structures

The paper proposed approaches to minimized embedding of the Hamiltonian graphs in the enveloping fault-tolerant graph representing the structural model of a fault-tolerant multiprocessor computer system. Failures are regarded as faults of vertices and/or connections between the graph vertices. Mathematical studies rely on the group-theoretical analysis of the characteristics of system structure. It underlies the proposed unique approach to designing the one-fault-tolerant and k-fault-tolerant structures retaining after reconfiguration the logical structure of the original target graph and, therefore, the compiled code of system tasks. The minimum fault-tolerant solutions were obtained for one-fault-tolerant and k-fault-tolerant cycles, simple and diagonal grids, and other popular structures, including arbitrary Hamiltonian graphs for which solutions are of minimized nature. Consideration was given to the algorithms of reconfiguration after arbitrary single and multiple faults. Restoration after faults is very simple; it is based on small tables of the group of system automorphisms which enable correct restoration of the system at the level of theorems without either static or dynamic additional verification of the reconfiguration process.     

Optimal algorithms for symmetry detection in two and three dimensions

Exact algorithms for detecting all rotational and involutional symmetries in point sets, polygons and polyhedra are described. The time complexities of the algorithms are shown to be (n) for polygons and (n logn) for two- and three-dimensional point sets. (n logn) time is also required for general polyhedra, but for polyhedra with connected, planar surface graphs (n) time can be achieved. All algorithms are optimal in time complexity, within constants.     

Traversal of an Unknown Directed Graph by a Finite Robot

A covering path in a directed graph is a path passing through all vertices and arcs of the graph, with each arc being traversed only in the direction of its orientation. A covering path exists for any initial vertex only if the graph is strongly connected, i.e., any of its vertices can be reached from any other vertex by some path. The strong connectivity is the only restriction on the considered class of graphs. As is known, on the class of such graphs, the covering path length is (nm), where n is the number of vertices and m is the number of arcs. For any graph, there exists a covering path of length O(nm), and there exist graphs with covering paths of the minimum length (nm). The traversal of an unknown graph implies that the topology of the graph is not a priori known, and we learn it only in the course of traversing the graph. At each vertex, one can see which arcs originate from the vertex, but one can learn to which vertex a given arc leads only after traversing this arc. This is similar to the problem of traversing a maze by a robot in the case where the plan of the maze is not available. If the robot is a general-purpose computer without any limitations on the number of its states, then traversal algorithms with the same estimate O(nm) are known. If the number of states is bounded, then this robot is a finite automaton. Such a robot is an analogue of the Turing machine, where the tape is replaced by a graph and the cells are assigned to the graph vertices and arcs. Currently, the lower estimate of the length of the traversal by a finite robot is not known. In 1971, the author of this paper suggested a robot with the traversal length O(nm + n ²logn). The algorithm of the robot is based on the construction of the output directed spanning tree of the graph and on the breadth-first search (BFS) on this tree. In 1993, Afek and Gafni [1] suggested an algorithm with the same estimate of the covering path length, which was also based on constructing a spanning tree but used the depth-first search (DFS) method. In this paper, an algorithm is suggested that combines the breadth-first search with the backtracking (suggested by Afek and Gafni), which made it possible to reach the estimate O(nm + n ²loglogn). The robot uses a constant number of memory bits for each vertex and arc of the graph.     

Reduction Algorithms for Graphs of Small Treewidth

This paper presents a number of new ideas and results on graph reduction applied to graphs of bounded treewidth. S. Arnborg, B. Courcelle, A. Proskurowski, and D. Seese (J. Assoc. Comput. Mach.40, 1134–1164 (1993)) have shown that many decision problems on graphs can be solved in linear time on graphs of bounded treewidth, using a finite set of reduction rules. These algorithms can be used to solve problems on graphs of bounded treewidth without the need to obtain a tree decomposition of the input graph first. We show that the reduction method can be extended to solve the construction variants of many decision problems on graphs of bounded treewidth, including all problems definable in monadic second order logic. We also show that a variant of these reduction algorithms can be used to solve (constructive) optimization problems in O(n) time. For example, optimization and construction variants of I S and H C N can be solved in this way on graphs of small treewidth. Additionally, we show that the results of H. L. Bodlaender and T. Hagerup (SIAM J. Comput.27, 1725–1746 (1998)) can be applied to our reduction algorithms, which results in parallel reduction algorithms that use O(n) operations and O(log n log* n) time on an EREW PRAM, or O(log n) time on a CRCW PRAM.     

Efficient parallel recognition of some circular arc graphs,I

We present the first efficient parallel algorithms for recognizing some subclasses of circular arc graphs including circular arc graphs and proper interval graphs. These algorithms run in O(log² n) time withO(n ³) processors on a CRCW PRAM. An intersection representation can also be constructed within the same resource bounds. Furthermore, we propose some new characterizations of circular arc graphs and proper interval graphs.Portions of this paper have appeared in preliminary form in theProceedings of the 1989 IEEE international Symposium on Circuits and Systems [9], theProceedings of the 1989 Workshop on Algorithms and Data Structures [10], and theProceedings of the 1990 Canadian Conference on Computational Geometry [11].     

Using partial evaluation in holistic subgraph search

Because of its wide application, the subgraph matching problem has been studied extensively during the past decade. However, most existing solutions assume that a data graph is a vertex/edge-labeled graph (i.e., each vertex/edge has a simple label). These solutions build structural indices by considering the vertex labels. However, some real graphs contain rich-content vertices such as user profiles in social networks and HTML pages on the World Wide Web. In this study, we consider the problem of subgraph matching using a more general scenario. We build a structural index that does not depend on any vertex content. Based on the index, we design a holistic subgraph matching algorithm that considers the query graph as a whole and finds one match at a time. In order to further improve efficiency, we propose a “partial evaluation and assembly” framework to find subgraph matches over large graphs. Last but not least, our index has light maintenance overhead. Therefore, our method can work well on dynamic graphs. Extensive experiments on real graphs show that our method outperforms the state-of-the-art algorithms.     

Multilayer grid embeddings for VLSI

In this paper we propose two new multilayer grid models for VLSI layout, both of which take into account the number of contact cuts used. For the first model in which nodes exist only on one layer, we prove a tight area × (number of contact cuts) = (n ²) tradeoff for embeddingn-node planar graphs of bounded degree in two layers. For the second model in which nodes exist simultaneously on all layers, we give a number of upper bounds on the area needed to embed groups using no contact cuts. We show that anyn-node graph of thickness 2 can be embedded on two layers inO(n ²) area. This bound is tight even if more layers and any number of contact cuts are allowed. We also show that planar graphs of bounded degree can be embedded on two layers inO(n ^3/2(logn)²) area.Some of our embedding algorithms have the additional property that they can respect prespecified grid placements of the nodes of the graph to be embedded. We give an algorithm for embeddingn-node graphs of thicknessk ink layers usingO(n ³) area, using no contact cuts, and respecting prespecified node placements. This area is asymptotically optimal for placement-respecting algorithms, even if more layers are allowed, as long as a fixed fraction of the edges do not use contact cuts. Our results use a new result on embedding graphs in a single-layer grid, namely an embedding ofn-node planar graphs such that each edge makes at most four turns, and all nodes are embedded on the same line.The first author's research was partially supported by NSF Grant No. MCS 820-5167.     

Embedding cycles and paths on solid grid graphs

Despite many algorithms for embedding graphs on unbounded grids, only a few results on embedding graphs on restricted grids have been published. In this paper, we study the problem of embedding paths and cycles on solid grid graphs. We show that a cycle of length k is unit-length embeddable on a solid grid graph G if k is an even integer between four and the length of the longest cycle of G. In addition, our result shows that a path of length k is unit-length embeddable on G, between its two given vertices s and t, if \(k\le L\) and \(k\equiv L (\mathrm{mod}\ 2)\), in which L is the length of the longest path between s and t. Our presented two algorithms show that such embeddings can be found in linear time for cycles and quadratic time for paths, with respect to the size of graph G. In the case of rectangular grid graphs, the running time of the algorithms can be improved to O(k) and O\((k^2)\), respectively. In addition, we extend our results to \(m\times n\times o\) 3D grids. A application of our result is in the interconnection network mapping in parallel processing.     

Experimental Analysis of Heuristic Algorithms for the Dominating Set Problem

We say a vertex v in a graph G covers a vertex w if v=w or if v and w are adjacent. A subset of vertices of G is a dominating set if it collectively covers all vertices in the graph. The dominating set problem, which is NP-hard, consists of finding a smallest possible dominating set for a graph. The straightforward greedy strategy for finding a small dominating set in a graph consists of successively choosing vertices which cover the largest possible number of previously uncovered vertices. Several variations on this greedy heuristic are described and the results of extensive testing of these variations is presented. A more sophisticated procedure for choosing vertices, which takes into account the number of ways in which an uncovered vertex may be covered, appears to be the most successful of the algorithms which are analyzed. For our experimental testing, we used both random graphs and graphs constructed by test case generators which produce graphs with a given density and a specified size for the smallest dominating set. We found that these generators were able to produce challenging graphs for the algorithms, thus helping to discriminate among them, and allowing a greater variety of graphs to be used in the experiments. Received October 27, 1998; revised March 25, 2001.     

Algorithms to Compute Minimum Cycle Basis in Directed Graphs

We consider the problem of computing a minimum cycle basis in a directed graph G with m arcs and n vertices. The arcs of G have non-negative weights assigned to them. In this problem a {-1,0,1} incidence vector is associated with each cycle and the vector space over generated by these vectors is the cycle space of G. A set of cycles is called a cycle basis of G if it forms a basis for its cycle space. A cycle basis where the sum of weights of the cycles is minimum is called a minimum cycle basis of G. This paper presents an algorithm, which is the first polynomial-time algorithm for computing a minimum cycle basis in G. We then improve it to an algorithm. The problem of computing a minimum cycle basis in an undirected graph has been well studied. In this problem a {0,1} incidence vector is associated with each cycle and the vector space over generated by these vectors is the cycle space of the graph. There are directed graphs in which the minimum cycle basis has lower weight than any cycle basis of the underlying undirected graph. Hence algorithms for computing a minimum cycle basis in an undirected graph cannot be used as black boxes to solve the problem in directed graphs.