| 000 | 05384cam a2200565Ki 4500 | ||
|---|---|---|---|
| 001 | 9781003033707 | ||
| 003 | FlBoTFG | ||
| 005 | 20240213122830.0 | ||
| 006 | m o d | ||
| 007 | cr cnu|||unuuu | ||
| 008 | 211007s2022 xx o 000 0 eng d | ||
| 040 |
_aOCoLC-P _beng _erda _epn _cOCoLC-P |
||
| 020 |
_a9781003033707 _q(electronic bk.) |
||
| 020 |
_a1003033709 _q(electronic bk.) |
||
| 020 | _z9781032169231 | ||
| 020 |
_a9781000538694 _q(electronic bk. : EPUB) |
||
| 020 |
_a1000538699 _q(electronic bk. : EPUB) |
||
| 020 |
_a9781000538618 _q(electronic bk. : PDF) |
||
| 020 |
_a1000538613 _q(electronic bk. : PDF) |
||
| 020 | _z9780367464127 | ||
| 035 | _a(OCoLC)1273728002 | ||
| 035 | _a(OCoLC-P)1273728002 | ||
| 050 | 4 | _aQA166 | |
| 072 | 7 |
_aBUS _x061000 _2bisacsh |
|
| 072 | 7 |
_aCOM _x000000 _2bisacsh |
|
| 072 | 7 |
_aCOM _x012040 _2bisacsh |
|
| 072 | 7 |
_aUMB _2bicssc |
|
| 082 | 0 | 4 |
_a511/.5 _223/eng/20211105 |
| 245 | 0 | 0 |
_aMassive graph analytics / _cedited by David A. Bader. |
| 250 | _aFirst edition. | ||
| 264 | 1 |
_a[Place of publication not identified] : _bChapman and Hall/CRC, _c2022. |
|
| 300 | _a1 online resource (544 pages). | ||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 490 | 0 | _aChapman & Hall/CRC Data Science Series | |
| 505 | 0 | _aAbout the EditorList of ContributorsIntroductionAlgorithms: Search and PathsA Work-Efficient Parallel Breadth-First Search Algorithm (or How to Cope With the Nondeterminism of Reducers)Charles E. Leiserson and Tao B. Schardl Multi-Objective Shortest PathsStephan Erb, Moritz Kobitzsch, Lawrence Mandow , and Peter Sanders Algorithms: Structure Multicore Algorithms for Graph Connectivity ProblemsGeorge M. Slota, Sivasankaran Rajamanickam, and Kamesh Madduri Distributed Memory Parallel Algorithms for Massive GraphsMaksudul Alam, Shaikh Arifuzzaman, Hasanuzzaman Bhuiyan, Maleq Khan, V.S. Anil Kumar, and Madhav Marathe Efficient Multi-core Algorithms for Computing Spanning Forests and Connected ComponentsFredrik Manne, Md. Mostofa Ali Patwary Massive-Scale Distributed Triangle Computation and ApplicationsGeoffrey Sanders, Roger Pearce, Benjamin W. Priest, Trevor Steil Algorithms and Applications Computing Top-k Closeness Centrality in Fully-dynamic GraphsEugenio Angriman, Patrick Bisenius, Elisabetta Bergamini, Henning Meyerhenke Ordering Heuristics for Parallel Graph ColoringWilliam Hasenplaugh, Tim Kaler, Tao B. Schardl, and Charles E. Leiserson Partitioning Trillion Edge GraphsGeorge M. Slota, Karen Devine, Sivasankaran Rajamanickam, Kamesh Madduri New Phenomena in Large-Scale Internet TrafficJeremy Kepner, Kenjiro Cho, KC Claffy, Vijay Gadepally, Sarah McGuire, Peter Michaleas, Lauren Milechin Parallel Algorithms for Butterfly ComputationsJessica Shi and Julian Shun Models Recent Advances in Scalable Network GenerationManuel Penschuck, Ulrik Brandes, Michael Hamann, Sebastian Lamm, Ulrich Meyer, Ilya Safro, Peter Sanders, and Christian Schulz Computational Models for Cascades in Massive Graphs: How to Spread a Rumor in ParallelAjitesh Srivastava, Charalampos Chelmis, Viktor K. Prasanna Executing Dynamic Data-Graph Computations Deterministically Using Chromatic SchedulingTim Kaler, William Hasenplaugh, Tao B. Schardl, and Charles E.Leiserson Frameworks and Software Graph Data Science Using Neo4jAmy E. Hodler, Mark Needham The Parallel Boost Graph Library 2.0Nicholas Edmonds and Andrew Lumsdaine RAPIDS cuGraphAlex Fender, Bradley Rees, Joe Eaton A Cloud-based approach to Big GraphsPaul Burkhardt and Christopher A. Waring Introduction to GraphBLASJeremy Kepner, Peter Aaltonen, David Bader, Aydin Buluc, Franz Franchetti, John Gilbert, Dylan Hutchinson, Manoj Kumar, Andrew Lumsdaine, Henning Meyerhenke, Scott McMillian, Jose Moreira, John D. Owens, Carl Yang, Marcin Zalewski, and Timothy G. MattsonGraphulo: Linear Algebra Graph KernelsVijay Gadepally, Jake Bolewski, Daniel Hook, Shana Hutchison, Benjamin A Miller, Jeremy Kepner Interactive Graph Analytics at Scale in ArkoudaZhihui Du, Oliver Alvarado Rodriguez, Joseph Patchett, and David A. Bader | |
| 520 | _aExpertise in massive scale graph analytics is key for solving real-world grand challenges from health to sustainability to detecting insider threats, cyber defense, and more. Massive Graph Analytics provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. The book will be beneficial to students, researchers and practitioners, in academia, national laboratories, and industry, who wish to learn about the state-of-the-art algorithms, models, frameworks, and software in massive scale graph analytics. | ||
| 588 | _aOCLC-licensed vendor bibliographic record. | ||
| 650 | 7 |
_aBUSINESS & ECONOMICS / Statistics _2bisacsh |
|
| 650 | 7 |
_aCOMPUTERS / General _2bisacsh |
|
| 650 | 7 |
_aCOMPUTERS / Computer Graphics / Game Programming & Design _2bisacsh |
|
| 650 | 0 |
_aGraph theory _xData processing. |
|
| 650 | 0 | _aGraph algorithms. | |
| 650 | 0 | _aBig data. | |
| 650 | 0 | _aData mining. | |
| 700 | 1 |
_aBader, David A., _d1969- _eeditor. |
|
| 856 | 4 | 0 |
_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003033707 |
| 856 | 4 | 2 |
_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf |
| 999 |
_c5696 _d5696 |
||