Flooding in dynamic graphs with arbitrary degree sequence

This paper addresses the flooding problem in dynamic graphs, where flooding is the basic mechanism in which every node becoming aware of a piece of information at step t$t$ forwards this information to all its neighbors at all forthcoming steps t^′>t$t^{\prime }>t$. We show that a technique developed in a previous paper, for analyzing flooding in a Markovian sequence of Erdös–Rényi graphs, is robust enough to be used also in different contexts. We establish this fact by analyzing flooding in a sequence of graphs drawn independently at random according to a model of random graphs with given expected degree sequence. In the prominent case of power-law degree distributions, we prove that flooding takes almost surely O(logn)$O(logn)$ steps even if, almost surely, none of the graphs in the sequence is connected. In the general case of graphs with an arbitrary degree sequence, we prove several upper bounds on the flooding time, which depend on specific properties of the degree sequence.     

Lower bounds for the broadcast problem in mobile radio networks

Summary.  In this paper, we prove a lower bound on the number of rounds required by a deterministic distributed protocol for broadcasting a message in radio networks whose processors do not know the identities of their neighbors. Such an assumption captures the main characteristic of mobile and wireless environments [3], i.e., the instability of the network topology. For any distributed broadcast protocol Π, for any n and for any D≦n/2, we exhibit a network G with n nodes and diameter D such that the number of rounds needed by Π for broadcasting a message in G is Ω(D log n). The result still holds even if the processors in the network use a different program and know n and D. We also consider the version of the broadcast problem in which an arbitrary number of processors issue at the same time an identical message that has to be delivered to the other processors. In such a case we prove that, even assuming that the processors know the network topology, Ω(n) rounds are required for solving the problem on a complete network (D=1) with n processors. Received: August 1994 / Accepted: August 1996     

New spectral lower bounds on the bisection width of graphs

The communication overhead is a major bottleneck for the execution of a process graph on a parallel computer system. In the case of two processors, the minimization of the communication can be modeled using the graph bisection problem. The spectral lower bound of λ₂|V|/4 for the bisection width of a graph is widely known. The bisection width is equal to λ₂|V|/4 iff all vertices are incident to λ₂/2 cut edges in every optimal bisection.

We present a new method of obtaining tighter lower bounds on the bisection width. This method makes use of the level structure defined by the bisection. We define some global expansion properties and we show that the spectral lower bound increases with this global expansion. Under certain conditions we obtain a lower bound depending on λ₂^β|V| with . We also present examples of graphs for which our new bounds are tight up to a constant factor. As a by-product, we derive new lower bounds for the bisection widths of 3- and 4-regular Ramanujan graphs.     

Hamiltonicity in connected regular graphs

In 1980, Jackson proved that every 2-connected k-regular graph with at most 3k vertices is Hamiltonian. This result has been extended in several papers. In this note, we determine the minimum number of vertices in a connected k-regular graph that is not Hamiltonian, and we also solve the analogous problem for Hamiltonian paths. Further, we characterize the smallest connected k-regular graphs without a Hamiltonian cycle.     

Lower bounds for asynchronous consensus

Impossibility results and best-case lower bounds are proved for the number of message delays and the number of processes required to reach agreement in an asynchronous consensus algorithm that tolerates non-Byzantine failures. General algorithms exist that achieve these lower bounds in the normal case, when the response time of non-faulty processes and the transmission delay of messages they send to one another are bounded. Our theorems allow algorithms to do better in certain exceptional cases, and such algorithms are presented. Two of these exceptional algorithms may be of practical interest.     

Large independent sets in random regular graphs

We present algorithmic lower bounds on the size _sd$s_d$ of the largest independent sets of vertices in random d$d$-regular graphs, for each fixed d≥3$d\ge 3$. For instance, for d=3$d=3$ we prove that, for graphs on n$n$ vertices, _sd≥0.43475n$s_d\ge 0.43475n$ with probability approaching one as n$n$ tends to infinity.     

Bounds on the bisection width for random d -regular graphs

In this paper we provide an explicit way to compute asymptotically almost sure upper bounds on the bisection width of random d$d$-regular graphs, for any value of d$d$. The upper bounds are obtained from the analysis of the performance of a randomized greedy algorithm to find bisections of d$d$-regular graphs. We provide bounds for 5≤d≤12$5\le d\le 12$. We also give empirical values of the size of the bisection found by the algorithm for some small values of d$d$ and compare them with numerical approximations of our theoretical bounds. Our analysis also gives asymptotic lower bounds for the size of the maximum bisection.     

On a game in directed graphs

Inspired by recent algorithms for electing a leader in a distributed system, we study the following game in a directed graph: each vertex selects one of its outgoing arcs (if any) and eliminates the other endpoint of this arc; the remaining vertices play on until no arcs remain. We call a directed graph lethal if the game must end with all vertices eliminated and mortal if it is possible that the game ends with all vertices eliminated. We show that lethal graphs are precisely collections of vertex-disjoint cycles, and that the problem of deciding whether or not a given directed graph is mortal is NP-complete (and hence it is likely that no “nice” characterization of mortal graphs exists).     

Generating synthetic task graphs for simulating stream computing systems

Stream-computing is an emerging computational model for performing complex operations on and across multi-source, high-volume data flows. The pool of mature publicly available applications employing this model is fairly small, and therefore the availability of workloads for various types of applications is scarce. Thus, there is a need for synthetic generation of large-scale workloads to drive simulations and estimate the performance of stream-computing applications at scale. We identify the key properties shared by most task graphs of stream-computing applications and use them to extend known random graph generation concepts with stream computing specific features, providing researchers with realistic input stream graphs. Our graph generation techniques serve the purpose of covering a disparity of potential applications and user input. Our first “domain-specific” framework exhibits high user-controlled configurability while the second “application-agnostic” framework focuses solely on emulating the key properties of general stream-computing systems, at the loss of domain-specific fine-tuning.     

Lower bounds on the OBDD size of two fundamental functions' graphs

Ordered Binary Decision Diagrams (OBDDs) are a data structure for Boolean functions which supports many useful operations. Among others it finds applications in CAD, model checking, and symbolic graph algorithms. Nevertheless, many simple functions are known to have exponential OBDD size with respect to their number of variables. In order to investigate the limits of symbolic graph algorithms which work on OBDD-represented graph instances, it is useful to have simply-structured graphs whose OBDD representation has exponential size. Therefore, we consider two fundamental functions with exponential lower bounds on their OBDD size and transfer these results to their corresponding graphs. Concretely, we consider the Indirect Storage Access function and the Hidden Weighted Bit function.     

Efficient leader election using sense of direction

Summary.  This paper presents a protocol for leader election in complete networks with a sense of direction. Sense of direction provides nodes the capability of distinguishing between their incident links according to a global scheme. We propose a protocol for leader election which requires O(N) messages and O(log N) time. The protocol is message optimal and the time complexity is a significant improvement over currently known protocols for this problem. Received August 1995 / Accepted December 1996     

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.     

随机图的点可区别全染色算法

点可区别全染色(VDTC)是指在满足正常全染色的基础上,还要使得图中由顶点颜色和其关联边颜色构成的顶点色集合也不同,所使用的最少颜色数称为点可区别全色数.提出了一种针对随机图的点可区别全染色算法,算法的基本思想是对图G中的边随机地进行预染色,查找存在边染色不正常的冲突集,然后根据规则逐步迭代,直至使目标函数的值满足要求,此时说明染色成功.实验结果表明,算法能够有效地求得给定点数随机图的点可区别全色数,算法时间复杂度不超过O(n3).     

On maximal 3-restricted edge connectivity of regular graphs

Let G be a k-regular graph of order at least nine. It is proved in this work that graph G is maximally 3-restricted edge-connected if it is triangle-free and k>|G|/4+1, the lower bound on k is sharp to some extent. With this observation, we present an expression of the number of edge cuts in the previous graph, which may be employed to analyse the reliability of telecommunication networks.     

Routing direction determination in regular networks based on configurable circuits

We consider the problem of finding short paths in a regular network such as a k-ary n-cube. This problem is a basic aspect of routing and has to be implemented with a very high performance for cluster and parallel computer networks. To achieve this, a scalable reconfigurable circuit is proposed that covers many popular topologies at acceptable cost. As a demonstration, the application to a modestly complex topology is shown in detail (“Multi-Mesh” introduced by Das et al. [D. Das, M. De, B.P. Sinha, A network topology with multiple meshes, IEEE Transactions on Computers 48 (5) (1999) 536–551]).

The achieved flexibility is much higher than that of previously reported reconfigurable circuits for the same purpose such as interval routing [R.B. Tan, J. van Leeuwen, Compact routing methods: a survey, in: Proceedings of the Colloquium on Structural Information and Communication Complexity (SICC94), School of Computer Science, Carleton University, Ottawa, 1995, pp. 99–109] or bit-pattern-associative routing [D.H. Summerville, J.G. Delgado-Frias, S. Vassiliadis, A flexible bit-pattern-associative router for interconnection networks, IEEE Transactions on Parallel and Distributed Systems 7 (5) (1996) 477–485]. The proposed circuit extends the domain of application-specific reconfigurable circuits beyond the areas of signal processing and cryptography, where most work is currently done.     

First order Gaussian graphs for efficient structure classification

First order random graphs as introduced by Wong are a promising tool for structure-based classification. Their complexity, however, hampers their practical application. We describe an extension to first order random graphs which uses continuous Gaussian distributions to model the densities of all random elements in a random graph. These First Order Gaussian Graphs (FOGGs) are shown to have several nice properties which allow for fast and efficient clustering and classification. Specifically, we show how the entropy of a FOGG may be computed directly from the Gaussian parameters of its random elements. This allows for fast and memoryless computation of the objective function used in the clustering procedure used for learning a graphical model of a class. We give a comparative evaluation between FOGGs and several traditional statistical classifiers. On our example problem, selected from the area of document analysis, our first order Gaussian graph classifier significantly outperforms statistical, feature-based classifiers. The FOGG classifier achieves a classification accuracy of approximately 98%, while the best statistical classifiers only manage approximately 91%.     

New bounds for multi-label interval routing

Interval routing (IR) is a space-efficient routing method for computer networks. For longest routing path analysis, researchers have focused on lower bounds for many years. For any n-node graph G of diameter D, there exists an upper bound of 2D for IR using one or more labels, and an upper bound of for IR using or more labels. We present two upper bounds in the first part of the paper. We show that for every integer i>0, every n-node graph of diameter D has a k-dominating set of size for . This result implies a new upper bound of for IR using or more labels, where i is any positive integer constant. We apply the result by Kutten and Peleg [8] to achieve an upper bound of (1+)D for IR using O(n/D) or more labels, where is any constant in (0,1). The second part of the paper offers some lower bounds for planar graphs. For any M-label interval routing scheme (M-IRS), where , we derive a lower bound of [(2M+1)/(2M)]D−1 on the longest path for , and a lower bound of [(2(1+δ)M+1)/(2(1+δ)M)]D, where δ(0,1], for . The latter result implies a lower bound of on the number of labels needed to achieve optimality.     

Labeling schemes for weighted dynamic trees

A Distance labeling scheme is a type of localized network representation in which short labels are assigned to the vertices, allowing one to infer the distance between any two vertices directly from their labels, without using any additional information sources. As most applications for network representations in general, and distance labeling schemes in particular, concern large and dynamically changing networks, it is of interest to focus on distributed dynamic labeling schemes. The paper considers dynamic weighted trees where the vertices of the trees are fixed but the (positive integral) weights of the edges may change. The two models considered are the edge-dynamic model, where from time to time some edge changes its weight by a fixed quanta, and the increasing-dynamic model in which edge weights can only grow. The paper presents distributed approximate distance labeling schemes for the two dynamic models, which are efficient in terms of the required label size and communication complexity involved in updating the labels following the weight changes.