In this paper, an analysis of the basic process of a class of interactive-graph-cut-based image segmentation algorithms indicates that it is unnecessary to construct n-links for all adjacent pixel nodes of an image before calculating the maximum flow and the minimal cuts. There are many pixel nodes for which it is not necessary to construct n-links at all. Based on this, we propose a new algorithm for the dynamic construction of all necessary n-links that connect the pixel nodes explored by the maximum flow algorithm. These n-links are constructed dynamically and without redundancy during the process of calculating the maximum flow. The Berkeley segmentation dataset benchmark is used to prove that this method can reduce the average running time of segmentation algorithms on the premise of correct segmentation results. This improvement can also be applied to any segmentation algorithm based on graph cuts.

Deploying and managing content objects efficiently is critical for building a scalable and transparent content delivery system. This paper investigates the advanced incremental deploying problem of which the objects are delivered in a successive manner. Recently, the researchers show that the minimum-cost content deployment can be obtained by reducing the problem to the well-known network flow problem. In this paper, the maximum flow algorithm for a single graph is extended to the incremental growing graph. Based on this extension, an efficient incremental content deployment algorithm is developed in this work.

In advance of network communication society by the internet, the way how to send data fast with a little loss becomes an important transportation problem. A generalized maximum flow algorithm gives the best solution for the transportation problem that which route is appropriated to exchange data. Therefore, the importance of the maximum flow algorithm is growing more and more. In this paper, we propose a Maximum-Flow Neural Network (MF-NN) in which branch nonlinearity has a saturation characteristic and by which the maximum flow problem can be solved with analog high-speed parallel processing. That is, the proposed neural network for the maximum flow problem can be realized by a nonlinear resistive circuit where each connection weight between nodal neurons has a sigmodal or piece-wise linear function. The parallel hardware of the MF-NN will be easily implemented.

A web community is a set of web pages that provide resources on a specific topic. Various methods for finding web communities based on link analysis have been proposed in the literature. The method proposed in this paper is based on the method using the maximum flow algorithm proposed in. Our objective of using the maximum flow algorithm is to extract a subgraph which can be recognized as a good web community in the context of the quantity and the quality. This paper first discusses the features of the maximum flow algorithm based method. The previously proposed approach has a problem that a certain graph structure containing noises (i.e., irrelevant pages) is always extracted. This problem is mainly caused by edge capacities assigned a constant value. This paper proposes an assignment of variable edge capacities that are based on hub and authority scores obtained from HITS calculation. To examine the effects of our proposed method, we performed experiments using a Japanese archive crawled in February 2002. Our experimental results demonstrate that our proposed method removes noise pages caused by constant edge capacities and improves the quality of web communities.

It is known that all minimum cuts in an edge-weighted undirected graph with n vertices and m edges can be represented by a cactus with O(n) vertices and edges, a connected graph in which each edge is contained in an exactly one cycle. In this paper, we show that such a cactus representation can be computed in O(mn+n2log n) time and O(m) space. This improves the previously best complexity of deterministic cactus construction algorithms, and matches with the time bound of the fastest deterministic algorithm for computing a single minimum cut.

In this paper, we propose a method for detecting conserved domains from a set of amino acid sequences that belong to a protein family. This method detects the domains as follows: first, generate fixed-length subsequences from the sequences; second, construct a weighted graph that connects any two of the subsequences (vertices) having higher similarity than a pre-defined threshold; third, search for the maximum-density subgraph for each connected component of the graph; finally, explore conserved domains in the sequences by combining the results of the previous step. From the performance results obtained by applying the method to several protein families that have complex conserved domains, we found that our method was able to detect those domains even though some domains were weakly conserved.

In this paper, we present a hierarchical technique for simultaneous pin assignment and global routing during floorplanning based on the minimum cost maximum integer flow algorithm with several heuristic cost functions. Furthermore, our algorithm handles feedthrough pins and equi-potential pins taking into account global routes. Our algorithm allows various user specified constraints such as pre-specified pin positions, wiring paths, wiring widths and critical nets. Experimental results including Xerox floorplanning benchmark have shown the effectiveness of the heuristics.

In this paper, we propose an algorithm of O(|V|min{k,|V|,|A|}|A|) time complexity for finding all k-edge-connected components of a given digraph D=(V,A) and a positive integer k. When D is symmetric, incorporating a preprocessing reduces this time complexity to O(|A|+|V|2+|V|min{k,|V|}min{k|V|,|A|}), which is at most O(|A|+k2|V|2).

A vertex-capacitated network is a graph whose edges and vertices have infinite positive capacities and finite positive capacities, respectively. Such a network is a model of a communication system in which capacities of links are much larger than those of stations. This paper considers a problem of realizing a flow-saturation in an undirected vertex-capacitated network by adding the least number of edges. By defining a set of influenced vertex pairs by adding edges, we show the follwing results.(1) It suffices to add the least number of edges to unsaturated vertex pairs for realizing flow-saturation.(2) An associated graph of a flow-unsaturated network defined in this paper gives us a sufficient condition that flow-saturation is realized by adding a single edge.

Location theory on networks is concerned with the problem of selecting the best location in a specified network for facilities. Many studies for the theory have been done. However, few studies treat location problems on networks from the standpoint of measuring the closeness between two vertices by the capacity (maximum flow value) between two vertices. This paper concerns location problems, called covering problems on flow networks. We define two types of covering problems on flow networks. We show that covering problems on undirected flow networks and a covering problem on directed flow networks are solved in polynomial times.

This paper deals with the problem of assigning tasks to the processors of a multiprocessor system such that the sum of execution and communication costs is minimized. If the number of processors is two, this problem can be solved efficiently using the network flow approach pioneered by Stone. This problem is, however, known to be NP-complete in the general case, and thus intractable for systems with a large number of processors. In this paper, we propose a network flow approach for the task assignment problem in homogeneous hypercube networks, i.e., hypercube networks with functionally identical processors. The task assignment problem for an n-dimensional homogeneous hypercube network of N (=2n) processors and M tasks is first transformed into n two-terminal network flow problems, and then solved in time no worse than O(M3 log N) by applying the Goldberg-Tarjan's maximum flow algorithm on each two-terminal network flow problem.