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020 _a9783030878863
_9978-3-030-87886-3
024 7 _a10.1007/978-3-030-87886-3
_2doi
050 4 _aQA75.5-76.95
072 7 _aUYA
_2bicssc
072 7 _aCOM014000
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072 7 _aUYA
_2thema
082 0 4 _a004.0151
_223
100 1 _aErciyes, K.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aAlgebraic Graph Algorithms
_h[electronic resource] :
_bA Practical Guide Using Python /
_cby K. Erciyes.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXIII, 221 p. 100 illus., 12 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUndergraduate Topics in Computer Science,
_x2197-1781
505 0 _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.
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.
650 0 _aComputer science.
650 0 _aComputer science
_xMathematics.
650 0 _aDiscrete mathematics.
650 1 4 _aTheory of Computation.
650 2 4 _aDiscrete Mathematics in Computer Science.
650 2 4 _aMathematical Applications in Computer Science.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030878856
776 0 8 _iPrinted edition:
_z9783030878870
830 0 _aUndergraduate Topics in Computer Science,
_x2197-1781
856 4 0 _uhttps://doi.org/10.1007/978-3-030-87886-3
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c184798
_d184798