About: Flow network is a research topic. Over the lifetime, 10925 publications have been published within this topic receiving 256957 citations.
Papers published on a yearly basis
01 Jan 1993
TL;DR: In-depth, self-contained treatments of shortest path, maximum flow, and minimum cost flow problems, including descriptions of polynomial-time algorithms for these core models are presented.
Abstract: A comprehensive introduction to network flows that brings together the classic and the contemporary aspects of the field, and provides an integrative view of theory, algorithms, and applications. presents in-depth, self-contained treatments of shortest path, maximum flow, and minimum cost flow problems, including descriptions of polynomial-time algorithms for these core models. emphasizes powerful algorithmic strategies and analysis tools such as data scaling, geometric improvement arguments, and potential function arguments. provides an easy-to-understand descriptions of several important data structures, including d-heaps, Fibonacci heaps, and dynamic trees. devotes a special chapter to conducting empirical testing of algorithms. features over 150 applications of network flows to a variety of engineering, management, and scientific domains. contains extensive reference notes and illustrations.
01 Jan 1962
TL;DR: Ford and Fulkerson as mentioned in this paper set the foundation for the study of network flow problems and developed powerful computational tools for solving and analyzing network flow models, and also furthered the understanding of linear programming.
Abstract: In this classic book, first published in 1962, L. R. Ford, Jr., and D. R. Fulkerson set the foundation for the study of network flow problems. The models and algorithms introduced in Flows in Networks are used widely today in the fields of transportation systems, manufacturing, inventory planning, image processing, and Internet traffic. The techniques presented by Ford and Fulkerson spurred the development of powerful computational tools for solving and analyzing network flow models, and also furthered the understanding of linear programming. In addition, the book helped illuminate and unify results in combinatorial mathematics while emphasizing proofs based on computationally efficient construction. Flows in Networks is rich with insights that remain relevant to current research in engineering, management, and other sciences. This landmark work belongs on the bookshelf of every researcher working with networks.
TL;DR: A key claim made in this paper is that centrality measures can be regarded as generating expected values for certain kinds of node outcomes given implicit models of how traffic flows, and that this provides a new and useful way of thinking about centrality.
Abstract: Centrality measures, or at least popular interpretations of these measures, make implicit assumptions about the manner in which traffic flows through a network. For example, some measures count only geodesic paths, apparently assuming that whatever flows through the network only moves along the shortest possible paths. This paper lays out a typology of network flows based on two dimensions of variation, namely the kinds of trajectories that traffic may follow (geodesics, paths, trails, or walks) and the method of spread (broadcast, serial replication, or transfer). Measures of centrality are then matched to the kinds of flows that they are appropriate for. Simulations are used to examine the relationship between type of flow and the differential importance of nodes with respect to key measurements such as speed of reception of traffic and frequency of receiving traffic. It is shown that the off-the-shelf formulas for centrality measures are fully applicable only for the specific flow processes they are designed for, and that when they are applied to other flow processes they get the “wrong” answer. It is noted that the most commonly used centrality measures are not appropriate for most of the flows we are routinely interested in. A key claim made in this paper is that centrality measures can be regarded as generating expected values for certain kinds of node outcomes (such as speed and frequency of reception) given implicit models of how traffic flows, and that this provides a new and useful way of thinking about centrality. © 2004 Elsevier B.V. All rights reserved.
01 Jan 1977
TL;DR: This chapter discusses the complexity of the Simplex Algorithms and their applications in linear algebra, convex analysis, and Polyhedral Sets.
Abstract: One: Introduction.Two: Linear Algebra, Convex Analysis, and Polyhedral Sets.Three: The Simplex Method.Four: Starting Solution and Convergence.Five: Special Simplex Implementations and Optimality Conditions.Six: Duality and Sensitivity Analysis.Seven: The Decomposition Principle.Eight: Complexity of the Simplex Algorithms.Nine: Minimal-Cost Network Flows.Ten: The Transportation and Assignment Problems.Eleven: The Out-of-Kilter Algorithm.Twelve: Maximal Flow, Shortest Path, Multicommodity Flow, and Network Synthesis Problems.Bibliography.Index.
01 Jan 2001
TL;DR: In this article, the authors presented new algorithms for the maximum flow problem, the Hitchcock transportation problem and the general minimum-cost flow problem and derived upper bounds on the number of steps in these algorithms.
Abstract: This paper presents new algorithms for the maximum flow problem, the Hitchcock transportation problem and the general minimum-cost flow problem. Upper bounds on the number of steps in these algorithms are derived, and are shown to improve on the upper bounds of earlier algorithms.
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