Finding Minimum Vertex Covering in Stochastic Graphs: A Learning Automata Approach

Structural and behavioral parameters of many real networks such as social networks are unpredictable, uncertain, and have time-varying parameters, and for these reasons, deterministic graphs for modeling such networks are too restrictive to solve most of the real-network problems. It seems that stochastic graphs, in which weights associated to the vertices are random variables, might be better graph models for real-world networks. Once we use a stochastic graph as the model for a network, every feature of the graph such as path, spanning tree, clique, dominating set, and cover set should be treated as a stochastic feature. For example, choosing a stochastic graph as a graph model of an online social network and defining community structure in terms of clique, the concept of a stochastic clique may be used to study community structures’ properties or define spreading of influence according to the coverage of influential users; the concept of stochastic vertex covering may be used to study spread of influence. In this article, minimum vertex covering in stochastic graphs is first defined, and then four learning, automata-based algorithms are proposed for solving a minimum vertex-covering problem in stochastic graphs where the probability distribution functions of the weights associated with the vertices of the graph are unknown. It is shown that through a proper choice of the parameters of the proposed algorithms, one can make the probability of finding minimum vertex cover in a stochastic graph as close to unity as possible. Experimental results on synthetic stochastic graphs reveal that at a certain confidence level the proposed algorithms significantly outperform the standard sampling method in terms of the number of samples needed to be taken from the vertices of the stochastic graph.     

On the structure of graphs in the Caucal hierarchy

We investigate the structure of graphs in the Caucal hierarchy. We provide criteria concerning the degree of vertices or the length of paths which can be used to show that a given graph does not belong to a certain level of this hierarchy. Each graph in the Caucal hierarchy corresponds to the configuration graph of some higher-order pushdown automaton. The main part of the paper consists of a study of such configuration graphs. We provide tools to decompose and reassemble their runs, and we prove a pumping lemma for higher-order pushdown automata.     

Reachability Problems on Regular Ground Tree Rewriting Graphs

We consider the transition graphs of regular ground tree (or term) rewriting systems. The vertex set of such a graph is a (possibly infinite) set of trees. Thus, with a finite tree automaton one can represent a regular set of vertices. It is known that the backward closure of sets of vertices under the rewriting relation preserves regularity, i.e., for a regular set T of vertices the set of vertices from which one can reach T can be accepted by a tree automaton. The main contribution of this paper is to lift this result to the recurrence problem, i.e., we show that the set of vertices from which one can reach infinitely often a regular set T is regular, too. Since this result is effective, it implies that the problem whether, given a tree t and a regular set T, there is a path starting in t that infinitely often reaches T, is decidable. Furthermore, it is shown that the problems whether all paths starting in t eventually (respectively, infinitely often) reach T, are undecidable. Based on the decidability result we define a fragment of temporal logic with a decidable model-checking problem for the class of regular ground tree rewriting graphs.     

Matrix approach to simplification of finite state machines using semi‐tensor product of matrices

In this paper, we use a new mathematical tool, semi‐tensor product of matrices, to investigate the problem of simplification of finite state machines (FSMs) in a mathematical manner. First, based on the dynamic equations of state transition and output behavior which are developed recently, an algebraic criterion of k‐difference states is established. Second, using the criterion, a scheme is designed to construct the incompatible graphs of FSMs. Third, with the incompatible graphs and the method of searching internally stable sets of graphs proposed by the authors, a solution is proposed to obtain all of the compatible state set (CSS) of FSMs. Then, with the aid of the CSS, we investigate three kinds of structures of state space of FSMs, including compatible cover of state set (CCSS), representative set of state set (RSSS), and minimum representative set of state set (MRSSS); necessary and sufficient conditions are proposed to formulate the three kinds of structures. Finally, examples are given to exemplify minimum realizations of FSMs by these conditions.     

Dynamic monopolies and feedback vertex sets in hexagonal grids

In a majority conversion process, the vertices of a graph can be in one of the two states, colored or uncolored, and these states are dynamically updated so that a vertex becomes colored at a certain time period if at least half of its neighbors were in the colored state in the previous time period. A dynamic monopoly is a set of vertices in a graph that when initially colored will eventually cause all vertices in the graph to become colored. This paper establishes a connection between dynamic monopolies and the well-known feedback vertex sets which are sets of vertices whose removal results in an acyclic graph. More specifically, we show that dynamic monopolies and feedback vertex sets are equivalent in graphs wherein all vertices have degree 2 or 3. We use this equivalence to provide exact values for the minimum size of dynamic monopolies of planar hexagonal grids, as well as upper and lower bounds on the minimum size of dynamic monopolies of cylindrical and toroidal hexagonal grids. For these last two topologies, the respective upper and lower bounds differ by at most one.     

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.     

On Making a Distinguished Vertex of Minimum Degree by Vertex Deletion

For directed and undirected graphs, we study how to make a distinguished vertex the unique minimum-(in)degree vertex through deletion of a minimum number of vertices. The corresponding NP-hard optimization problems are motivated by applications concerning control in elections and social network analysis. Continuing previous work for the directed case, we show that the problem is W[2]-hard when parameterized by the graph’s feedback arc set number, whereas it becomes fixed-parameter tractable when combining the parameters “feedback vertex set number” and “number of vertices to delete”. For the so far unstudied undirected case, we show that the problem is NP-hard and W[1]-hard when parameterized by the “number of vertices to delete”. On the positive side, we show fixed-parameter tractability for several parameterizations measuring tree-likeness. In particular, we provide a dynamic programming algorithm for graphs of bounded treewidth and a vertex-linear problem kernel with respect to the parameter “feedback edge set number”. On the contrary, we show a non-existence result concerning polynomial-size problem kernels for the combined parameter “vertex cover number and number of vertices to delete”, implying corresponding non-existence results when replacing vertex cover number by treewidth or feedback vertex set number.     

Toward continuous pattern detection over evolving large graph with snapshot isolation

This paper studies continuous pattern detection over large evolving graphs, which plays an important role in monitoring-related applications. The problem is challenging due to the large size and dynamic updates of graphs, the massive search space of pattern detection and inconsistent query results on dynamic graphs. This paper first introduces a snapshot isolation requirement, which ensures that the query results come from a consistent graph snapshot instead of a mixture of partial evolving graphs. Second, we propose an SSD (single sink directed acyclic graph) plan friendly to vertex-centric-distributed graph processing frameworks. SSD plan can guide the message transformation and transfer among graph vertices, and determine the satisfaction of the pattern on graph vertices for the sink vertex. Third, we devise strategies for major steps in the SSD evaluation, including the location of valid messages to achieve snapshot isolation, AO-List to determine the satisfaction of transition rule over dynamic graph, and message-on-change policy to reduce outgoing messages. The experiments on billion-edge graphs using Giraph, an open source implementation of Pregel, illustrate the efficiency and effectiveness of our method.     

Stochastic minority on graphs

Cellular automata have been mainly studied on very regular graphs carrying the vertices (like lines or grids) and under synchronous dynamics (all vertices update simultaneously). In this paper, we study how the asynchronism and the graph act upon the dynamics of the classical minority rule. Minority has been well-studied for synchronous updates and is thus a reasonable choice to begin with. Yet, beyond its apparent simplicity, this rule yields complex behaviors when asynchronism is introduced. We investigate the transitory part as well as the asymptotic behavior of the dynamics under full asynchronism (also called sequential: only one random vertex updates at each time step) for several types of graphs. Such a comparative study is a first step in understanding how the asynchronous dynamics is linked to the topology (the graph).Previous analyses on the grid Regnault et al. (2009, 2010) [1] and [2] have observed that minority seems to induce fast stabilization. We investigate here this property on arbitrary graphs using tools such as energy, particles and random walks. We show that the worst case convergence time is, in fact, strongly dependent on the topology. In particular, we observe that the case of trees is nontrivial.     

On the power of synchronization between two adjacent processes

We study the power of local computations on labelled edges (which allow two adjacent vertices to synchronize and to modify their states simultaneaously in function of their previous states) through the classical election problem. We characterize the graphs for which this problem has a solution. As corollaries we characterize graphs which admit an election algorithm for two seminal models: Angluin’s model and asynchronous systems where processes communicate with synchronous message passing (i.e., there is a synchronization between the process sending the message and the one receiving it).     

Maximum Weight Independent Sets in hole- and co-chair-free graphs

The Maximum Weight Independent Set (MWIS) problem on graphs with vertex weights asks for a set of pairwise nonadjacent vertices of maximum total weight. Being one of the most investigated and most important problems on graphs, it is well known to be $\mathbb{NP}$-complete and hard to approximate. The complexity of MWIS is open for hole-free graphs (i.e., graphs without induced subgraphs isomorphic to a chordless cycle of length at least five). By applying a combination of clique separator and modular decomposition, we obtain a polynomial time solution of MWIS for hole- and co-chair-free graphs (the co-chair consists of five vertices four of which form a clique minus one edge – a diamond – and the fifth has degree one and is adjacent to one of the degree two vertices of the diamond).     

Two algorithms for determining a minimum independent dominating set

The problem of determining a minimum independent dominating set is fundamental to both the theory and applications of graphs. Computationally it belongs to the class of hard combinatorial optimization problems known as NP-hard. In this paper, we develop a backtracking algorithm and a dynamic programming algorithm to determine a minimum independent dominating set. Computational experience with the backtracking algorithm on more than 1000 randomly generated graphs ranging from 100 to 200 vertices and from 10% to 60% densities has shown that the algorithm is effective.     

A refined search tree technique for Dominating Set on planar graphs

We establish a refined search tree technique for the parameterized DOMINATING SET problem on planar graphs. Here, we are given an undirected graph and we ask for a set of at most k vertices such that every other vertex has at least one neighbor in this set. We describe algorithms with running times O(8^kn) and O(8^kk+n³), where n is the number of vertices in the graph, based on bounded search trees. We describe a set of polynomial time data-reduction rules for a more general “annotated” problem on black/white graphs that asks for a set of k vertices (black or white) that dominate all the black vertices. An intricate argument based on the Euler formula then establishes an efficient branching strategy for reduced inputs to this problem. In addition, we give a family examples showing that the bound of the branching theorem is optimal with respect to our reduction rules. Our final search tree algorithm is easy to implement; its analysis, however, is involved.     

An optimal algorithm for solving the searchlight guarding problem on weighted two-terminal series-parallel graphs

In this paper, we consider a graph problem on a connected weighted undirected graph, called the searchlight guarding problem. Our problem is an extension of so-called graph searching/guarding problem by considering the time slot parameter in addition to the traditional building cost. Suppose that there is a fugitive who moves along the edges of the graph at any speed. We want to place a set of searchlights at the vertices to search the edges of the graph and capture the fugitive. It costs some building cost to place a searchlight at some vertex. The searchlight guarding problem is to allocate a set S of searchlights at the vertices such that the total costs of the vertices in S is minimized. If there is more than one set of searchlights with the minimum building cost, then find the one with the minimum searching time, that is, the time slots needed to capture the fugitive is the minimum. The problem is known to be NP-hard on weighted bipartite graphs, split graphs, and chordal graphs; and it is linear time solvable on weighted trees and interval graphs. In this paper, an algorithm is designed to solve the problem on weighted two-terminal series-parallel graphs. It works on the parsing tree structure of the given two-terminal series-parallel graph. The algorithm is divided into two phases. In the phase one, we first extract some useful properties of optimal solutions. Employing these properties, an algorithm is designed to find the set of searchlights with the minimum guarding cost and to assign the searching directions of all edges by the dynamic programming strategy. In the phase two, the searched time slots of all edges are determined by the breadth-first-search from the root of the parsing tree. The time complexities of both phases are linear. Thus, our algorithm is time optimal. Received: 12 March 1996 / 27 May 1997     

Maximum gap labelings of graphs

Given a graph, we define a base set to be a set of integers of size equal to the number of vertices in the graph. Given a graph and a base set, a labeling of the graph from the base set is an assignment of distinct integers from the base set to the vertices of the graph. The gap of an edge in a labeled graph is the absolute value of the difference between the labels of its endpoints. The gap of a labeled graph is the sum of the gaps of its edges.The maximum gap graph labeling problem takes as input a graph and a base set and maximizes the gap of the graph over all possible labelings from the base set. We show that this problem is NP-complete even when the base set is restricted to consecutive integers. We also show that this restricted case has polynomial time approximations that achieve a factor of 2/3 for trees, of 1/2 for bipartite graphs, and of 1/4 for general graphs, with a deterministic algorithm, while an expected factor of 1/3 for general graphs is achieved with a randomized algorithm. The case of general base sets is approximated within an expected factor of 1/16 for general graphs with a randomized polynomial time algorithm. We finally give a polynomial time algorithm that solves the maximum gap graph labeling problem for a graph that has bounded degree and bounded treewidth. The maximum graph labeling problem shows connections with the graceful tree conjecture.     

基于搜索树的平面图支配集算法

许多来自工业应用的优化问题都是NP难问题。确定参数可解FPT作为处理这类问题的另外一种思路,在最近的10多年中受到了广泛的关注。支配集问题是图论中最重要的NP完全的组合优化问题之一,即使对于FPT体系而言,一般图中的支配集问题属于W[2]完全的,意味着不可能设计出复杂度为f(k)no(1)的算法。在本文中,我们考虑在给定的平面图G=(V,E)中参数化支配集问题,给定参数k,看是否存在大小为k的顶点集合支配图中的其他顶点,当把问题限定在平面图上,这个问题属于确定参数可解。本文给出了基于两组归约规则的搜索树算法,通过使用规约技术化简实例,构造搜索树,得到了复杂度为O(8kn)的算法,同时通过相关实验结果显示了归约规则对算法的作用。     

Identifying functional modules using generalized directed graphs: Definition and application

We extend traditional directed graphs to generalized directed graphs, making them capable of representing function structures as graphs. In a generalized directed graph, vectors are used to denote the edges, which are pairs of sub-function vertices connected by a relationship, and elements of the vectors indicate different types of flow (i.e., material, energy, or signal) on which sub-functions operate. Based on these definitions, we formalize the three heuristics proposed by Stone et al. into rules to identify functional modules in function structures: (1) sequential flow rule, (2) parallel flow rule and (3) flow transformation rule. The arithmetic for identifying functional modules based on these formalized rules is developed, and a computer-aided software tool is created to facilitate this process. Finally, the proposed approach is applied to a function structure for a power screwdriver, and the results compare favorably to those obtained using the three heuristics.     

图同构的矩阵初等变换判定及算法设计

判断图同构的一种有用的方法是对图的邻接矩阵进行初等变换,变成另一个图的邻接矩阵。不幸的是,当初等变换后两个矩阵不能相等时,并不能说明两个图不同构,因为可能存在另一种变换途径,使得两个矩阵相等。另一方面,这种穷尽变换途径的方法有n!种可能(n为图的顶点个数);当n太大时,尝试每一种可能来说明两个图是否同构是不可行的,是一个NP难问题。文章提出了一个简单有效的判断图同构的方法。首先,利用邻接矩阵生成行码距异或矩阵和行码距同或矩阵;其次,寻找邻接矩阵、行码距异或矩阵、行码距同或矩阵间保持行元素一样的行-行置换;如果这种置换存在,则图同构,否则不同构。最后,根据行-行置换确定出同构函数,它给出了两个图的顶点间具有保持相邻关系的一一对应。     

An optimal algorithm for solving the searchlight guarding problem on weighted interval graphs

In this paper, a graph problem on connected, weighted, undirected graphs, called the searchlight guarding problem, is considered. Assume that there is a fugitive who moves along the edges of the graph at a random speed. The task involves placing a set of searchlights at vertices to search the edges of the graph and to spot the fugitive. Suppose that placing a searchlight at some vertex incurs some building cost. The searchlight guarding problem is to allocate a set S of searchlights at the vertices such that the total cost of the vertices in S is minimized. If there is more than one set of searchlights, each with a minimum building cost, then identify the set with the minimum search time, that is, where the time slots needed to spot the fugitive is the minimum. As is well established, the problem is NP-hard on weighted bipartite graphs but is linear-time solvable on weighted trees. In this paper, the design of a linear-time optimal algorithm for the searchlight guarding problem on weighted interval graphs is presented. It entails two phases. In the first phase, a set of searchlights with minimum guarding cost is identified and the search directions of all edges are assigned. To achieve this task, a new problem, called the edge-direction assignment problem, is first defined and the problem on weighted complete-split graphs is solved by the greedy strategy. Based on this computational result, the problem of finding the set of searchlights with minimum guarding cost and assigning the search directions of all edges is solved by the dynamic programming strategy. Then, in the second phase, the search time slots of each edge are determined on the basis of the results of the first phase and on some properties of interval graphs.     

Graph matching using the interference of continuous-time quantum walks

We consider how continuous-time quantum walks can be used for graph matching. We focus in detail on both exact and inexact graph matching, and consider in depth the problem of measuring graph similarity. We commence by constructing an auxiliary graph, in which the two graphs to be matched are co-joined by a layer of indicator vertices (one for each potential correspondence between a pair of vertices). We simulate a continuous-time quantum walk in parallel on the two graphs. The layer of connecting indicator vertices in the auxiliary graph allow quantum interference to take place between the two walks. The interference amplitudes on the indicator vertices are determined by differences in the two walks, and can be used to calculate probabilities for matches between pairs of vertices from the graphs. By applying the Hungarian (Kuhn-Munkres) algorithm to these probabilities, we recover a correspondence mapping between the graphs. To calculate graph similarity, we combine these probabilities with edge-consistency information to give a consistency measure. Based on the consistency measure, we define two graph similarity measures, one of which requires correspondence matches while the second does not. We analyse our approach experimentally using synthetic and real-world graphs. This reveals that our method gives results that are intermediate between the most sophisticated iterative techniques available, and simpler less complex ones.