Synopsis
This clearly structured textbook/reference presents a detailed and comprehensive review of the fundamental principles of sequential graph algorithms, approaches for NP-hard graph problems, approximation algorithms and heuristics for such problems and implementation of advanced graph structures in machine learning. The work also provides a comparative analysis of sequential, parallel and distributed graph algorithms – including algorithms for big data – and an investigation into the conversion principles between the three algorithmic methods.
Topics and features:
- Presents a comprehensive analysis of sequential graph algorithms
- Offers a unifying view by examining the same graph problem from each of the three paradigms of sequential, parallel and distributed algorithms
- Describes methods for the conversion between sequential, parallel and distributed graph algorithms
- Surveys methods for the analysis of large graphs and complex network applications
- Includes full implementation details for the problems presented throughout the text
- Surveys advanced graph structures used in artificial intelligence with code examples
- Reviews graph machine-intelligence methods
This practical guide to the design and analysis of graph algorithms is ideal for advanced and graduate students of computer science, electrical and electronic engineering, and bioinformatics. The material covered will also be of value to any researcher familiar with the basics of discrete mathematics, graph theory and algorithms and machine learning.
Dr. K. Erciyes is professor of computer engineering at Yaşar University, Turkey. His other publications include the Springer titles Distributed Graph Algorithms for Computer Networks, Distributed and Sequential Algorithms for Bioinformatics, and Guide to Distributed Algorithms.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Dr. K. Erciyes is professor of computer engineering at Yaşar University, Turkey. His other publications include the Springer titles Distributed Graph Algorithms for Computer Networks, Distributed and Sequential Algorithms for Bioinformatics and Guide to Distributed Algorithms.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Guide to Graph Algorithms (eng)
impression à la demandeVendeur : Brook Bookstore On Demand, Napoli, NA, Italie
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Etat : new. Questo è un articolo print on demand. N° de réf. du vendeur Z1BC3XHYVZ
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Guide to Graph Algorithms (Hardcover)
Edité par
Springer Nature Switzerland AG, Cham, 2026
ISBN 10 : 3032052939
ISBN 13 : 9783032052933
Neuf
Couverture rigide
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
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Hardcover. Etat : new. Hardcover. This clearly structured textbook/reference presents a detailed and comprehensive review of the fundamental principles of sequential graph algorithms, approaches for NP-hard graph problems, approximation algorithms and heuristics for such problems and implementation of advanced graph structures in machine learning. The work also provides a comparative analysis of sequential, parallel and distributed graph algorithms including algorithms for big data and an investigation into the conversion principles between the three algorithmic methods.Topics and features:Presents a comprehensive analysis of sequential graph algorithmsOffers a unifying view by examining the same graph problem from each of the three paradigms of sequential, parallel and distributed algorithmsDescribes methods for the conversion between sequential, parallel and distributed graph algorithmsSurveys methods for the analysis of large graphs and complex network applicationsIncludes full implementation details for the problems presented throughout the textSurveys advanced graph structures used in artificial intelligence with code examplesReviews graph machine-intelligence methods This practical guide to the design and analysis of graph algorithms is ideal for advanced and graduate students of computer science, electrical and electronic engineering, and bioinformatics. The material covered will also be of value to any researcher familiar with the basics of discrete mathematics, graph theory and algorithms and machine learning.Dr. K. Erciyes is professor of computer engineering at Yasar University, Turkey. His other publications include the Springer titles Distributed Graph Algorithms for Computer Networks, Distributed and Sequential Algorithms for Bioinformatics, and Guide to Distributed Algorithms. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783032052933
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Guide to Graph Algorithms
Edité par
Springer-Verlag Gmbh Jul 2026, 2026
ISBN 10 : 3032052939
ISBN 13 : 9783032052933
Neuf
Couverture rigide
Vendeur : Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Neuware -This clearly structured textbook/reference presents a detailed and comprehensive review of the fundamental principles of sequential graph algorithms, approaches for NP-hard graph problems, approximation algorithms and heuristics for such problems and implementation of advanced graph structures in machine learning. The work also provides a comparative analysis of sequential, parallel and distributed graph algorithms including algorithms for big data and an investigation into the conversion principles between the three algorithmic methods.Topics and features:Presents a comprehensive analysis of sequential graph algorithmsOffers a unifying view by examining the same graph problem from each of the three paradigms of sequential, parallel and distributed algorithmsDescribes methods for the conversion between sequential, parallel and distributed graph algorithmsSurveys methods for the analysis of large graphs and complex network applicationsIncludes full implementation details for the problems presented throughout the textSurveys advanced graph structures used in artificial intelligence with code examplesReviews graph machine-intelligence methods This practical guide to the design and analysis of graph algorithms is ideal for advanced and graduate students of computer science, electrical and electronic engineering, and bioinformatics. The material covered will also be of value to any researcher familiar with the basics of discrete mathematics, graph theory and algorithms and machine learning.Dr. K. Erciyesis professor of computer engineering at Yasar University, Turkey. His other publications include the Springer titles Distributed Graph Algorithms for Computer Networks, Distributed and Sequential Algorithms for Bioinformatics,and Guide to Distributed Algorithms. 529 pp. Englisch. N° de réf. du vendeur 9783032052933
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Guide to Graph Algorithms
Edité par
Springer-Verlag Gmbh Jul 2026, 2026
ISBN 10 : 3032052939
ISBN 13 : 9783032052933
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Neuware -This clearly structured textbook/reference presents a detailed and comprehensive review of the fundamental principles of sequential graph algorithms, approaches for NP-hard graph problems, approximation algorithms and heuristics for such problems and implementation of advanced graph structures in machine learning. The work also provides a comparative analysis of sequential, parallel and distributed graph algorithms including algorithms for big data and an investigation into the conversion principles between the three algorithmic methods.Topics and features:Presents a comprehensive analysis of sequential graph algorithmsOffers a unifying view by examining the same graph problem from each of the three paradigms of sequential, parallel and distributed algorithmsDescribes methods for the conversion between sequential, parallel and distributed graph algorithmsSurveys methods for the analysis of large graphs and complex network applicationsIncludes full implementation details for the problems presented throughout the textSurveys advanced graph structures used in artificial intelligence with code examplesReviews graph machine-intelligence methods This practical guide to the design and analysis of graph algorithms is ideal for advanced and graduate students of computer science, electrical and electronic engineering, and bioinformatics. The material covered will also be of value to any researcher familiar with the basics of discrete mathematics, graph theory and algorithms and machine learning.Dr. K. Erciyesis professor of computer engineering at Yasar University, Turkey. His other publications include the Springer titles Distributed Graph Algorithms for Computer Networks, Distributed and Sequential Algorithms for Bioinformatics,and Guide to Distributed Algorithms. 529 pp. Englisch. N° de réf. du vendeur 9783032052933
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Expédition à EUR 23
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Guide to Graph Algorithms: Sequential, Parallel and Distributed
Edité par
Springer-Nature New York Inc, 2026
ISBN 10 : 3032052939
ISBN 13 : 9783032052933
Neuf
Couverture rigide
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Hardcover. Etat : Brand New. 2nd edition. 552 pages. 9.25x6.10x9.49 inches. In Stock. N° de réf. du vendeur __3032052939
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Guide to Graph Algorithms
Edité par
Springer-Verlag Gmbh Jul 2026, 2026
ISBN 10 : 3032052939
ISBN 13 : 9783032052933
Neuf
Couverture rigide
Vendeur : Wegmann1855, Zwiesel, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Neuware -This clearly structured textbook/reference presents a detailed and comprehensive review of the fundamental principles of sequential graph algorithms, approaches for NP-hard graph problems, approximation algorithms and heuristics for such problems and implementation of advanced graph structures in machine learning. The work also provides a comparative analysis of sequential, parallel and distributed graph algorithms including algorithms for big data and an investigation into the conversion principles between the three algorithmic methods.Topics and features:- Presents a comprehensive analysis of sequential graph algorithms- Offers a unifying view by examining the same graph problem from each of the three paradigms of sequential, parallel and distributed algorithms- Describes methods for the conversion between sequential, parallel and distributed graph algorithms- Surveys methods for the analysis of large graphs and complex network applications- Includes full implementation details for the problems presented throughout the text- Surveys advanced graph structures used in artificial intelligence with code examples- Reviews graph machine-intelligence methods This practical guide to the design and analysis of graph algorithms is ideal for advanced and graduate students of computer science, electrical and electronic engineering, and bioinformatics. The material covered will also be of value to any researcher familiar with the basics of discrete mathematics, graph theory and algorithms and machine learning.Dr. K. Erciyes is professor of computer engineering at Yäar University, Turkey. His other publications include the Springer titles Distributed Graph Algorithms for Computer Networks, Distributed and Sequential Algorithms for Bioinformatics, and Guide to Distributed Algorithms. N° de réf. du vendeur 9783032052933
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GUIDE TO GRAPH ALGORITHMS
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Guide to Graph Algorithms
Edité par
Springer Verlag GmbH, 2026
ISBN 10 : 3032052939
ISBN 13 : 9783032052933
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
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Guide to Graph Algorithms (Hardcover)
Edité par
Springer Nature Switzerland AG, Cham, 2026
ISBN 10 : 3032052939
ISBN 13 : 9783032052933
Neuf
Couverture rigide
Vendeur : AussieBookSeller, Truganina, VIC, Australie
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : new. Hardcover. This clearly structured textbook/reference presents a detailed and comprehensive review of the fundamental principles of sequential graph algorithms, approaches for NP-hard graph problems, approximation algorithms and heuristics for such problems and implementation of advanced graph structures in machine learning. The work also provides a comparative analysis of sequential, parallel and distributed graph algorithms including algorithms for big data and an investigation into the conversion principles between the three algorithmic methods.Topics and features:Presents a comprehensive analysis of sequential graph algorithmsOffers a unifying view by examining the same graph problem from each of the three paradigms of sequential, parallel and distributed algorithmsDescribes methods for the conversion between sequential, parallel and distributed graph algorithmsSurveys methods for the analysis of large graphs and complex network applicationsIncludes full implementation details for the problems presented throughout the textSurveys advanced graph structures used in artificial intelligence with code examplesReviews graph machine-intelligence methods This practical guide to the design and analysis of graph algorithms is ideal for advanced and graduate students of computer science, electrical and electronic engineering, and bioinformatics. The material covered will also be of value to any researcher familiar with the basics of discrete mathematics, graph theory and algorithms and machine learning.Dr. K. Erciyes is professor of computer engineering at Yasar University, Turkey. His other publications include the Springer titles Distributed Graph Algorithms for Computer Networks, Distributed and Sequential Algorithms for Bioinformatics, and Guide to Distributed Algorithms. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9783032052933
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Guide to Graph Algorithms: Sequential, Parallel and Distributed (Texts in Computer Science)
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