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Graphs, Algorithms, and Optimization is a comprehensive book that features a clear explanation of modern graph theory in a rigorous yet approachable way. Suitable for use as a textbook in either mathematics or computer science courses, it is designed to present graph theory from an algorithmic point of view, allowing for a variety of technical applications. It includes useful programming techniques to solve graph theory problems, and applicable data structures to show how algorithms can be programmed. Additional coverage is provided on discrete optimization and its connection to graph theory, algorithmic complexity and efficiency, NP- completeness, and linear optimization.
Graph algorithms --- Algorithmes de graphes --- Graph algorithms.
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This book contains Volumes 4 and 5 of the Journal of Graph Algorithms and Applications (JGAA). The first book of this series, Graph Algorithms and Applications 1, published in March 2002, contains Volumes 1-3 of JGAA. JGAA is a peer-reviewed scientific journal devoted to the publication of high-quality research papers on the analysis, design, implementation, and applications of graph algorithms. Areas of interest include computational biology, computational geometry, computer graphics, computer-aided design, computer and interconnection networks, constraint
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Shimon Even's Graph Algorithms, published in 1979, was a seminal introductory book on algorithms read by everyone engaged in the field. This thoroughly revised second edition, with a foreword by Richard M. Karp and notes by Andrew V. Goldberg, continues the exceptional presentation from the first edition and explains algorithms in a formal but simple language with a direct and intuitive presentation. The book begins by covering basic material, including graphs and shortest paths, trees, depth-first-search and breadth-first search. The main part of the book is devoted to network flows and applications of network flows, and it ends with chapters on planar graphs and testing graph planarity.
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Algorithmic graph theory has been expanding at an extremely rapid rate since the middle of the twentieth century, in parallel with the growth of computer science and the accompanying utilization of computers, where efficient algorithms have been a prime goal. This book presents material on developments on graph algorithms and related concepts that will be of value to both mathematicians and computer scientists, at a level suitable for graduate students, researchers and instructors. The fifteen expository chapters, written by acknowledged international experts on their subjects, focus on the application of algorithms to solve particular problems. All chapters were carefully edited to enhance readability and standardize the chapter structure as well as the terminology and notation. The editors provide basic background material in graph theory, and a chapter written by the book's Academic Consultant, Martin Charles Golumbic (University of Haifa, Israel), provides background material on algorithms as connected with graph theory.
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Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.
Machine learning --- Graph algorithms --- Machine learning. --- Graph algorithms.
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Quantitative methods (economics) --- Graph algorithms. --- Graph theory
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This book contains volumes 1-3 of the Journal of Graph Algorithms and Applications (JGAA). Topics of interest include design and analysis of graph algorithms, experiences with graph algorithms, and applications of graph algorithms. JGAA is supported by distinguished advisory and editorial boards, has high scientific standards, and takes advantage of current electronic document technology. The electronic version of JGAA is available on the Web at http://www.cs.brown.edu/publications/jgaa/.
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Computer science --- Discrete mathematics --- Graph algorithms --- Algorithmes de graphes --- Graph algorithms.
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Beginning with its origins in the pioneering work of W.T. Tutte in 1947, this monograph systematically traces through some of the impressive developments in matching theory. A graph is matchable if it has a perfect matching. A matching covered graph is a connected graph on at least two vertices in which each edge is covered by some perfect matching. The theory of matching covered graphs, though of relatively recent vintage, has an array of interesting results with elegant proofs, several surprising applications and challenging unsolved problems. The aim of this book is to present the material in a well-organized manner with plenty of examples and illustrations so as to make it accessible to undergraduates, and also to unify the existing theory and point out new avenues to explore so as to make it attractive to graduate students.
Graph theory. --- Graph Theory. --- Graph theory --- Graph algorithms. --- Data processing.
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