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001441171 001__ 1441171
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001441171 020__ $$a9783030878863$$q(electronic bk.)
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001441171 0247_ $$a10.1007/978-3-030-87886-3$$2doi
001441171 035__ $$aSP(OCoLC)1287135608
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001441171 050_4 $$aQA166.245
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001441171 1001_ $$aErciyes, K.,$$eauthor.
001441171 24510 $$aAlgebraic graph algorithms :$$ba practical guide using Python /$$cK. Erciyes.
001441171 260__ $$aCham, Switzerland :$$bSpringer,$$c2021.
001441171 300__ $$a1 online resource (229 pages)
001441171 336__ $$atext$$btxt$$2rdacontent
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001441171 347__ $$bPDF
001441171 4901_ $$aUndergraduate topics in computer science
001441171 504__ $$aIncludes bibliographical references and index.
001441171 5050_ $$a1. Introduction -- 2. Graphs, Matrices and Matroids -- 3. Parallel Matrix Algorithm Kernel -- 4. Basic Graph Algorithms -- 5. Connectivity, Matching and Matroids -- 6. Subgraph Search -- 7. Analysis of Large Graphs -- 8. Clustering in Complex Networks -- 9. Kronecker Graphs -- 10. Sample Algorithms for Complex Networks.
001441171 506__ $$aAccess limited to authorized users.
001441171 520__ $$aThere has been unprecedented growth in the study of graphs, which are discrete structures that have many real-world applications. The design and analysis of algebraic algorithms to solve graph problems have many advantages, such as implementing results from matrix algebra and using the already available matrix code for sequential and parallel processing. Providing Python programming language code for nearly all algorithms, this accessible textbook focuses on practical algebraic graph algorithms using results from matrix algebra rather than algebraic study of graphs. Given the vast theory behind the algebraic nature of graphs, the book strives for an accessible, middle-ground approach by reviewing main algebraic results that are useful in designing practical graph algorithms on the one hand, yet mostly using graph matrices to solve the graph problems. Python is selected for its simplicity, efficiency and rich library routines; and with the code herein, brevity is forsaken for clarity. Topics and features: Represents graphs by algebraic structures, enabling new, robust methods for algorithm analysis and design Provides matroid-based solutions to some graph problems, including greedy algorithm problems Offers Python code that can be tested and modified for various inputs Supplies practical hints, where possible, for parallel processing associated with algebraic algorithms Links to a web page with supportive materials This clearly arranged textbook will be highly suitable for upper-level undergraduate students of computer science, electrical and electronic engineering, bioinformatics, and any researcher or person with background in discrete mathematics, basic graph theory and algorithms. Dr. Kayhan Erciyes is a full Professor in the Department of Software Engineering at Maltepe University, Istanbul, Turkey. His other publications include the Springer titles Discrete Mathematics and Graph Theory, Distributed Real-Time Systems, Guide to Graph Algorithms, Distributed and Sequential Algorithms for Bioinformatics, and Distributed Graph Algorithms for Computer Networks.
001441171 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 6, 2021).
001441171 650_0 $$aGraph algorithms.
001441171 650_0 $$aPython (Computer program language)
001441171 650_6 $$aAlgorithmes de graphes.
001441171 650_6 $$aPython (Langage de programmation)
001441171 655_0 $$aElectronic books.
001441171 77608 $$iPrint version:$$aErciyes, K.$$tAlgebraic Graph Algorithms.$$dCham : Springer International Publishing AG, ©2021$$z9783030878856
001441171 830_0 $$aUndergraduate topics in computer science.
001441171 852__ $$bebk
001441171 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-87886-3$$zOnline Access$$91397441.1
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001441171 980__ $$aEBOOK
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