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Thurston 210 $aNew York$cAcademic Press$d1972 610 0 $aSuono 610 0 $aAcustica 676 $a534 700 1$aMason,$bWarren Perry$012561 702 1$aThurston,$bRobert Norton 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001120190403321 952 $a26-047.010$b19840$fFI1 959 $aFI1 996 $aPhysical Acoustics$9336141 997 $aUNINA DB $aING01 LEADER 04690nam 22006735 450 001 9910510574003321 005 20240708150732.0 010 $a9783030878863$b(electronic bk.) 010 $z9783030878856 024 7 $a10.1007/978-3-030-87886-3 035 $a(MiAaPQ)EBC6809651 035 $a(Au-PeEL)EBL6809651 035 $a(CKB)19919500900041 035 $a(OCoLC)1287135608 035 $a(DE-He213)978-3-030-87886-3 035 $a(PPN)258839597 035 $a(EXLCZ)9919919500900041 100 $a20211117d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAlgebraic Graph Algorithms $eA Practical Guide Using Python /$fby K. Erciyes 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (229 pages) 225 1 $aUndergraduate Topics in Computer Science,$x2197-1781 311 08$aPrint version: Erciyes, K. Algebraic Graph Algorithms Cham : Springer International Publishing AG,c2021 9783030878856 327 $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. 330 $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. 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