-
GET 12% OFF
- Publisher's listprice EUR 80.24
-
33 279 Ft (31 694 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 12% (cc. 3 993 Ft off)
- Discounted price 29 285 Ft (27 891 Ft + 5% VAT)
29 285 Ft
Availability
Not yet published.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 2
- Publisher Springer Nature Switzerland
- Date of Publication 29 April 2026
- ISBN 9783032052933
- Binding Hardback
- No. of pages490 pages
- Size 235x155 mm
- Language English
- Illustrations X, 490 p. 265 illus., 200 illus. in color. 700
Categories
Long description:
"
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.
" MoreTable of Contents:
1. Introduction to Graphs.- 2. Graph Algorithms.- 3. Parallel Graph Algorithms.- 4. Distributed Graph Algorithms.- 5. Trees and Graph Traversals.- 6. Weighted Graphs.- 7. Connectivity.- 8. Matching.- 9. Independence, Domination and Vertex Cover.- 10. Coloring.
More
