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      1. Data och IT
      2. Systemvetenskap och AI

      Massive Graph Analytics

      AvDavid A. Bader

      Inbunden, Engelska, 2022

      Del i serien Chapman & Hall/CRC Data Science Series

      2 518 kr

      Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

      Beskrivning

      "Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an editor who knows most people working with graphs, and have that editor gather nearly 70 researchers to summarize their work with graphs. The result is the book Massive Graph Analytics." — Timothy G. Mattson, Senior Principal Engineer, Intel CorpExpertise in massive-scale graph analytics is key for solving real-world grand challenges from healthcare to sustainability to detecting insider threats, cyber defense, and more. This book provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government.Massive Graph Analytics 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.

      Produktinformation

      • Utgivningsdatum:2022-07-20
      • Mått:178 x 254 x 38 mm
      • Vikt:1 300 g
      • Format:Inbunden
      • Språk:Engelska
      • Serie:Chapman & Hall/CRC Data Science Series
      • Antal sidor:590
      • Förlag:Taylor & Francis Ltd
      • ISBN:9780367464127

      Utforska kategorier

      • Systemvetenskap och AI inom Data och IT
      • Databaser inom Data och IT
      • Hårdvara inom Data och IT

      Mer om författaren

      David A.Bader is a Distinguished Professor in the Department of Computer Science in the Ying Wu College of Computing and Director of the Institute for Data Science at New Jersey Institute of Technology. Prior to this, he served as founding Professor and Chair of the School of Computational Science and Engineering, College of Computing, at Georgia Institute of Technology. He is a Fellow of the IEEE, ACM, AAAS, and SIAM, and a recipient of the IEEE Sidney Fernbach Award.

      Recensioner i media

      Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an editor who knows most people working with graphs, and have that editor gather 68 researchers to summarize their work with Graphs. The result is the book Massive Graph Analytics. -- Timothy G Mattson, Senior Principal Engineer, Intel Corp

      Innehållsförteckning

      • About 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. SchardlMulti-Objective Shortest PathsStephan Erb, Moritz Kobitzsch, Lawrence Mandow , and Peter SandersAlgorithms: StructureMulticore Algorithms for Graph Connectivity ProblemsGeorge M. Slota, Sivasankaran Rajamanickam, and Kamesh MadduriDistributed Memory Parallel Algorithms for Massive GraphsMaksudul Alam, Shaikh Arifuzzaman, Hasanuzzaman Bhuiyan, Maleq Khan, V.S. Anil Kumar, and Madhav MaratheEfficient Multi-core Algorithms for Computing Spanning Forests and Connected ComponentsFredrik Manne, Md. Mostofa Ali PatwaryMassive-Scale Distributed Triangle Computation and ApplicationsGeoffrey Sanders, Roger Pearce, Benjamin W. Priest, Trevor SteilAlgorithms and Applications Computing Top-k Closeness Centrality in Fully-dynamic GraphsEugenio Angriman, Patrick Bisenius, Elisabetta Bergamini, Henning MeyerhenkeOrdering Heuristics for Parallel Graph ColoringWilliam Hasenplaugh, Tim Kaler, Tao B. Schardl, and Charles E. LeisersonPartitioning Trillion Edge GraphsGeorge M. Slota, Karen Devine, Sivasankaran Rajamanickam, Kamesh MadduriNew Phenomena in Large-Scale Internet TrafficJeremy Kepner, Kenjiro Cho, KC Claffy, Vijay Gadepally, Sarah McGuire, Lauren Milechin, William Arcand, David Bestor, William Bergeron, Chansup Byun, Matthew Hubbell, Michael Houle, Michael Jones, Andrew Prout, Albert Reuther, Antonio Rosa, Siddharth Samsi, Charles Yee, and Peter Michaleas, details the authors’ collection and curation of the largest publicly-available Internet traffic datasets. Parallel Algorithms for Butterfly ComputationsJessica Shi and Julian ShunModels Recent Advances in Scalable Network GenerationManuel Penschuck, Ulrik Brandes, Michael Hamann, Sebastian Lamm, Ulrich Meyer, Ilya Safro, Peter Sanders, and Christian SchulzComputational Models for Cascades in Massive Graphs: How to Spread a Rumor in ParallelAjitesh Srivastava, Charalampos Chelmis, Viktor K. PrasannaExecuting Dynamic Data-Graph Computations Deterministically Using Chromatic SchedulingTim Kaler, William Hasenplaugh, Tao B. Schardl, and Charles E. LeisersonFrameworks and SoftwareGraph Data Science Using Neo4jAmy E. Hodler, Mark NeedhamThe Parallel Boost Graph Library 2.0Nicholas Edmonds and Andrew LumsdaineRAPIDS cuGraphAlex Fender, Bradley Rees, Joe EatonA Cloud-based approach to Big GraphsPaul Burkhardt and Christopher A. WaringIntroduction 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 KepnerInteractive Graph Analytics at Scale in ArkoudaZhihui Du, Oliver Alvarado Rodriguez, Joseph Patchett, and David A. Bader
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