63,260 research outputs found

    An Alternative Approach to Counting Minimum (s; t)-cuts in Planar Graphs

    Get PDF
    Finding and counting minimum cuts in graphs can be useful in image processing and segmentation and in networking systems such as computer or road networks. Researchers have previously developed polynomial-time algorithms to count minimum cuts in planar graphs which utilize the relationship between maximum network flows and minimum cuts. This thesis presents a polynomial-time algorithm to count the number of minimum (s,t)-cuts in a planar graph without first finding a maximum flow. The presented algorithm is dependent on the finding that (s,t)-cuts in a planar graph correlate to certain cycles found in the dual of that graph, which can be efficiently counted

    Shortest path and maximum flow problems in planar flow networks with additive gains and losses

    Full text link
    In contrast to traditional flow networks, in additive flow networks, to every edge e is assigned a gain factor g(e) which represents the loss or gain of the flow while using edge e. Hence, if a flow f(e) enters the edge e and f(e) is less than the designated capacity of e, then f(e) + g(e) = 0 units of flow reach the end point of e, provided e is used, i.e., provided f(e) != 0. In this report we study the maximum flow problem in additive flow networks, which we prove to be NP-hard even when the underlying graphs of additive flow networks are planar. We also investigate the shortest path problem, when to every edge e is assigned a cost value for every unit flow entering edge e, which we show to be NP-hard in the strong sense even when the additive flow networks are planar

    NC Algorithms for Computing a Perfect Matching and a Maximum Flow in One-Crossing-Minor-Free Graphs

    Full text link
    In 1988, Vazirani gave an NC algorithm for computing the number of perfect matchings in K3,3K_{3,3}-minor-free graphs by building on Kasteleyn's scheme for planar graphs, and stated that this "opens up the possibility of obtaining an NC algorithm for finding a perfect matching in K3,3K_{3,3}-free graphs." In this paper, we finally settle this 30-year-old open problem. Building on recent NC algorithms for planar and bounded-genus perfect matching by Anari and Vazirani and later by Sankowski, we obtain NC algorithms for perfect matching in any minor-closed graph family that forbids a one-crossing graph. This family includes several well-studied graph families including the K3,3K_{3,3}-minor-free graphs and K5K_5-minor-free graphs. Graphs in these families not only have unbounded genus, but can have genus as high as O(n)O(n). Our method applies as well to several other problems related to perfect matching. In particular, we obtain NC algorithms for the following problems in any family of graphs (or networks) with a one-crossing forbidden minor: ∙\bullet Determining whether a given graph has a perfect matching and if so, finding one. ∙\bullet Finding a minimum weight perfect matching in the graph, assuming that the edge weights are polynomially bounded. ∙\bullet Finding a maximum stst-flow in the network, with arbitrary capacities. The main new idea enabling our results is the definition and use of matching-mimicking networks, small replacement networks that behave the same, with respect to matching problems involving a fixed set of terminals, as the larger network they replace.Comment: 21 pages, 6 figure

    Max flow vitality in general and stst-planar graphs

    Full text link
    The \emph{vitality} of an arc/node of a graph with respect to the maximum flow between two fixed nodes ss and tt is defined as the reduction of the maximum flow caused by the removal of that arc/node. In this paper we address the issue of determining the vitality of arcs and/or nodes for the maximum flow problem. We show how to compute the vitality of all arcs in a general undirected graph by solving only 2(n−1)2(n-1) max flow instances and, In stst-planar graphs (directed or undirected) we show how to compute the vitality of all arcs and all nodes in O(n)O(n) worst-case time. Moreover, after determining the vitality of arcs and/or nodes, and given a planar embedding of the graph, we can determine the vitality of a `contiguous' set of arcs/nodes in time proportional to the size of the set.Comment: 12 pages, 3 figure

    Transport Processes on Homogeneous Planar Graphs with Scale-Free Loops

    Full text link
    We consider the role of network geometry in two types of diffusion processes: transport of constant-density information packets with queuing on nodes, and constant voltage-driven tunneling of electrons. The underlying network is a homogeneous graph with scale-free distribution of loops, which is constrained to a planar geometry and fixed node connectivity k=3k=3. We determine properties of noise, flow and return-times statistics for both processes on this graph and relate the observed differences to the microscopic process details. Our main findings are: (i) Through the local interaction between packets queuing at the same node, long-range correlations build up in traffic streams, which are practically absent in the case of electron transport; (ii) Noise fluctuations in the number of packets and in the number of tunnelings recorded at each node appear to obey the scaling laws in two distinct universality classes; (iii) The topological inhomogeneity of betweenness plays the key role in the occurrence of broad distributions of return times and in the dynamic flow. The maximum-flow spanning trees are characteristic for each process type.Comment: 14 pages, 5 figure
    • …
    corecore