Efficient robust algorithms for the Maximum Weight Stable Set Problem in chair-free graph classes

Modular decomposition of graphs is a powerful tool for designing efficient algorithms for problems on graphs such as Maximum Weight Stable Set (MWS) and Maximum Weight Clique. Using this tool we obtain O(n·m) time algorithms for MWS on chair- and xbull-free graphs which considerably extend an earlier result on bull- and chair-free graphs by De Simone and Sassano (the chair is the graph with vertices a,b,c,d,e and edges ab,bc,cd,be, and the xbull is the graph with vertices a,b,c,d,e,f and edges ab,bc,cd,de,bf,cf). Moreover, our algorithm is robust in the sense that we do not have to check in advance whether the input graphs are indeed chair- and xbull-free.     

Algorithms for P<Subscript>4</Subscript>-Comparability Graph Recognition and Acyclic P<Subscript>4</Subscript>-Transitive Orientation

We consider two problems pertaining to P₄-comparability graphs, namely, the problem of recognizing whether a simple undirected graph is a P₄-comparability graph and the problem of producing an acyclic P₄-transitive orientation of a P₄-comparability graph. These problems have been considered by Hoàng and Reed who described O(n⁴)- and O(n⁵)-time algorithms for their solution, respectively, where n is the number of vertices of the input graph. Faster algorithms have recently been presented by Raschle and Simon, and by Nikolopoulos and Palios; the time complexity of these algorithms for either problem is O(n + m²), where m is the number of edges of the graph. In this paper we describe O(n m)-time and O(n + m)-space algorithms for the recognition and the acyclic P₄-transitive orientation problems on P₄-comparability graphs. The algorithms rely on properties of the P₄-components of a graph, which we establish, and on the efficient construction of the P₄-components by means of the BFS-trees of the complement of the graph rooted at each of its vertices, without however explicitly computing the complement. Both algorithms are simple and use simple data structures.     

On α-redundant vertices in P5-free graphs

We prove that Maximum Stable Set can be solved in polynomial time on two new subclasses of P₅-free graphs, extending some known polynomially solvable cases.     

A linear-time algorithm to compute a MAD tree of an interval graph

The average distance of a connected graph G is the average of the distances between all pairs of vertices of G. We present a linear time algorithm that determines, for a given interval graph G, a spanning tree of G with minimum average distance (MAD tree). Such a tree is sometimes referred to as a minimum routing cost spanning tree.     

Genetic operators for hierarchical graph clustering

In this paper we propose an encoding scheme and ad hoc operators for a genetic approach to hierarchical graph clustering. Given a connected graph whose vertices correspond to points within a Euclidean space and a fitness function, a hierarchy of graphs in which each vertex corresponds to a connected subgraph of the graph below is generated. Both the number of clustering levels and the number of clusters on each level are not fixed a priori and are subject to optimization.     

Expected parallel time and sequential space complexity of graph and digraph problems

This paper determines upper bounds on the expected time complexity for a variety of parallel algorithms for undirected and directed random graph problems. For connectivity, biconnectivity, transitive closure, minimum spanning trees, and all pairs minimum cost paths, we prove the expected time to beO(log logn) for the CRCW PRAM (this parallel RAM machine allows resolution of write conflicts) andO(logn · log logn) for the CREW PRAM (which allows simultaneous reads but not simultaneous writes). We also show that the problem of graph isomorphism has expected parallel timeO(log logn) for the CRCW PRAM andO(logn) for the CREW PRAM. Most of these results follow because of upper bounds on the mean depth of a graph, derived in this paper, for more general graphs than was known before.For undirected connectivity especially, we present a new probabilistic algorithm which runs on a randomized input and has an expected running time ofO(log logn) on the CRCW PRAM, withO(n) expected number of processors only.Our results also improve known upper bounds on the expected space required for sequential graph algorithms. For example, we show that the problems of finding connected components, transitive closure, minimum spanning trees, and minimum cost paths have expected sequential spaceO(logn · log logn) on a deterministic Turing Machine. We use a simulation of the CRCW PRAM to get these expected sequential space bounds.This research was supported by National Science Foundation Grant DCR-85-03251 and Office of Naval Research Contract N00014-80-C-0647.This research was partially supported by the National Science Foundation Grants MCS-83-00630, DCR-8503497, by the Greek Ministry of Research and Technology, and by the ESPRIT Basic Research Actions Project ALCOM.     

Optimal parallel algorithm for findingst-ambitus of a planar biconnected graph

A cycleC passing through two specific verticess andt of a biconnected graph is said to be anst-ambitus if its bridges do not interlace in some special way. We present algorithms forst-ambitus for planar biconnected graphs, which are much simpler than the one known for general graphs [MT]. Our algorithm runs inO(n) time on a sequential machine and (logn) parallel time usingO(n/logn) processors on an EREW PRAM.     

A linear-time algorithm for finding a sparsek-connected spanning subgraph of ak-connected graph

We show that anyk-connected graphG = (V, E) has a sparsek-connected spanning subgraphG = (V, E) with ¦E¦ =O(k¦V¦) by presenting anO(¦E¦)-time algorithm to find one such subgraph, where connectivity stands for either edge-connectivity or node-connectivity. By using this algorithm as preprocessing, the time complexities of some graph problems related to connectivity can be improved. For example, the current best time boundO(max{k ²¦V¦^1/2,k¦V¦}¦E¦) to determine whether node-connectivityK(G) of a graphG = (V, E) is larger than a given integerk or not can be reduced toO(max{k ³¦V¦^3/2,k ²¦V¦²}).The first author was partially supported by the Grant-in-Aid for Encouragement of Young Scientists of the Ministry of Education, Science and Culture of Japan and by the subvention to young scientists by the Research Foundation of Electrotechnology of Chubu.     

On approximating the longest path in a graph

We consider the problem of approximating the longest path in undirected graphs. In an attempt to pin down the best achievable performance ratio of an approximation algorithm for this problem, we present both positive and negative results. First, a simple greedy algorithm is shown to find long paths in dense graphs. We then consider the problem of finding paths in graphs that are guaranteed to have extremely long paths. We devise an algorithm that finds paths of a logarithmic length in Hamiltonian graphs. This algorithm works for a much larger class of graphs (weakly Hamiltonian), where the result is the best possible. Since the hard case appears to be that of sparse graphs, we also consider sparse random graphs. Here we show that a relatively long path can be obtained, thereby partially answering an open problem of Broderet al. To explain the difficulty of obtaining better approximations, we also prove hardness results. We show that, for any ε<1, the problem of finding a path of lengthn-n ^ε in ann-vertex Hamiltonian graph isNP-hard. We then show that no polynomial-time algorithm can find a constant factor approximation to the longest-path problem unlessP=NP. We conjecture that the result can be strengthened to say that, for some constant δ>0, finding an approximation of ration ^δ is alsoNP-hard. As evidence toward this conjecture, we show that if any polynomial-time algorithm can approximate the longest path to a ratio of , for any ε>0, thenNP has a quasi-polynomial deterministic time simulation. The hardness results apply even to the special case where the input consists of bounded degree graphs. D. Karger was supported by an NSF Graduate Fellowship, NSF Grant CCR-9010517, and grants from the Mitsubishi Corporation and OTL. R. Motwani was supported by an Alfred P. Sloan Research Fellowship, an IBM Faculty Development Award, grants from Mitsubishi and OTL, NSF Grant CCR-9010517, and NSF Young Investigator Award CCR-9357849, with matching funds from IBM, the Schlumberger Foundation, the Shell Foundation, and the Xerox Corporation, G. D. S. Ramkumar was supported by a grant from the Toshiba Corporation. Communicated by M. X. Goemans.     

Distributed computation of the knn graph for large high-dimensional point sets

High-dimensional problems arising from robot motion planning, biology, data mining, and geographic information systems often require the computation of k nearest neighbor (knn) graphs. The knn graph of a data set is obtained by connecting each point to its k closest points. As the research in the above-mentioned fields progressively addresses problems of unprecedented complexity, the demand for computing knn graphs based on arbitrary distance metrics and large high-dimensional data sets increases, exceeding resources available to a single machine. In this work we efficiently distribute the computation of knn graphs for clusters of processors with message passing. Extensions to our distributed framework include the computation of graphs based on other proximity queries, such as approximate knn or range queries. Our experiments show nearly linear speedup with over 100 processors and indicate that similar speedup can be obtained with several hundred processors.     

Fast recognition of Baxter permutations using syntactical and complete bipartite composite dag's

A correspondence is developed between permutations and two-dimensional digraphs. When the graphs are also required to be completely bipartite composite (CBC), their permutations then correspond to a class called the Baxter permutations. A permutation can be tested for the Baxter condition by a linear time processing of its digraph. This processing involves a pair of traversals of the digraph and is made possible by exploiting a relationship with the syntactical graphs used in language theory.     

String and graph grammar characterizations of bounded regular languages

Two grammatical characterizations of the bounded regular languages are presented: one in terms of graph grammars, the other using string grammars. First it is shown that a class of state graphs recognizing the bounded regular languages can be generated by a particular second-order contextfree graph grammar. Next we call uniquely recursive a right-linear (string) grammar having at most one right-recursive production for each of its nonterminals. It is then established that the class of languages generated by uniquely recursive, sequential right-linear grammars is exactly the bounded regular languages. Some comments on the relationship between string and graph grammars are made.     

A parallel algorithm for generating bicompatible elimination orderings of proper interval graphs

In this paper, we first show how a certain ordering of vertices, called bicompatible elimination ordering (BCO), of a proper interval graph can be used to solve efficiently several problems in proper interval graphs. We, then, propose an NC parallel algorithm (i.e., polylogarithmic-time employing a polynomial number of processors) in SIMD CRCW PRAM (Single Instruction Stream Multiple Data Stream Concurrent Read Concurrent Write Parallel Random Access Machine) model of parallel computation to compute a BCO of a proper interval graph. To the best of our knowledge, this is the first NC parallel algorithm to compute a BCO of a proper interval graph.     

Sparse feature selection based on graph Laplacian for web image annotation

Confronted with the explosive growth of web images, the web image annotation has become a critical research issue for image search and index. Sparse feature selection plays an important role in improving the efficiency and performance of web image annotation. Meanwhile, it is beneficial to developing an effective mechanism to leverage the unlabeled training data for large-scale web image annotation. In this paper we propose a novel sparse feature selection framework for web image annotation, namely sparse Feature Selection based on Graph Laplacian (FSLG)². FSLG applies the l_2,1/2-matrix norm into the sparse feature selection algorithm to select the most sparse and discriminative features. Additional, graph Laplacian based semi-supervised learning is used to exploit both labeled and unlabeled data for enhancing the annotation performance. An efficient iterative algorithm is designed to optimize the objective function. Extensive experiments on two web image datasets are performed and the results illustrate that our method is promising for large-scale web image annotation.     

Nanoplates of α-SnWO4 and SnW3O9 prepared via a facile hydrothermal method and their gas-sensing property

Nanoplates of α-SnWO₄ and SnW₃O₉ were selectively synthesized in large scale via a facile hydrothermal reaction method. The final products obtained were dependent on the reaction pH and the molar ratio of W⁶⁺ to Sn²⁺ in the precursors. The as-prepared nanoplates of α-SnWO₄ and SnW₃O₉ were characterized by X-ray powder diffraction (XRD), N₂-sorption BET surface area, transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM) and X-ray photoelectron spectroscopy (XPS). The XPS results showed that Sn exists in divalent form (Sn²⁺) in SnW₃O₉ as well as in α-SnWO₄. The gas-sensing performances of the as-prepared α-SnWO₄ and SnW₃O₉ toward H₂S and H₂ were investigated. The hydrothermal prepared α-SnWO₄ showed higher response toward H₂ than that prepared via a solid-state reaction due to the high specific surface area. The gas-sensing property toward H₂S as well as H₂ over SnW₃O₉ was for the first time reported. As compared to α-SnWO₄, SnW₃O₉ exhibits higher response toward H₂S and its higher response can be well explained by the existence of the multivalent W (W⁶⁺/W⁴⁺) in SnW₃O₉.     

Approximation of graph edit distance based on Hausdorff matching

Graph edit distance is a powerful and flexible method for error-tolerant graph matching. Yet it can only be calculated for small graphs in practice due to its exponential time complexity when considering unconstrained graphs. In this paper we propose a quadratic time approximation of graph edit distance based on Hausdorff matching. In a series of experiments we analyze the performance of the proposed Hausdorff edit distance in the context of graph classification and compare it with a cubic time algorithm based on the assignment problem. Investigated applications include nearest neighbor classification of graphs representing letter drawings, fingerprints, and molecular compounds as well as hidden Markov model classification of vector space embedded graphs representing handwriting. In many cases, a substantial speedup is achieved with only a minor loss in accuracy or, in one case, even with a gain in accuracy. Overall, the proposed Hausdorff edit distance shows a promising potential in terms of flexibility, efficiency, and accuracy.