Massive Graph Analytics
1st Edition
Published on 20. July 2022
Book
Hardback
590 pages
978-0-367-46412-7 (ISBN)
Description
"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 Corp
Expertise 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.
- Timothy G. Mattson, Senior Principal Engineer, Intel Corp
Expertise 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.
Reviews / Votes
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
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
College/higher education
Academic and Professional Reference
Illustrations
207 s/w Abbildungen, 207 s/w Zeichnungen, 47 s/w Tabellen
47 Tables, black and white; 207 Line drawings, black and white; 207 Illustrations, black and white
Dimensions
Height: 260 mm
Width: 183 mm
Thickness: 38 mm
Weight
1366 gr
ISBN-13
978-0-367-46412-7 (9780367464127)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
David A. Bader
Massive Graph Analytics
E-Book
07/2022
1st Edition
Chapman & Hall/CRC
€60.49
Available for download
David A. Bader
Massive Graph Analytics
E-Book
07/2022
1st Edition
Chapman & Hall/CRC
€60.49
Available for download
Person
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.
Content
About the Editor
List of Contributors
Introduction
Algorithms: Search and Paths
A 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 Paths
Stephan Erb, Moritz Kobitzsch, Lawrence Mandow , and Peter Sanders
Algorithms: Structure
Multicore Algorithms for Graph Connectivity Problems
George M. Slota, Sivasankaran Rajamanickam, and Kamesh Madduri
Distributed Memory Parallel Algorithms for Massive Graphs
Maksudul Alam, Shaikh Arifuzzaman, Hasanuzzaman Bhuiyan, Maleq Khan, V.S. Anil Kumar, and Madhav Marathe
Efficient Multi-core Algorithms for Computing Spanning Forests and Connected Components
Fredrik Manne, Md. Mostofa Ali Patwary
Massive-Scale Distributed Triangle Computation and Applications
Geoffrey Sanders, Roger Pearce, Benjamin W. Priest, Trevor Steil
Algorithms and Applications
Computing Top-k Closeness Centrality in Fully-dynamic Graphs
Eugenio Angriman, Patrick Bisenius, Elisabetta Bergamini, Henning Meyerhenke
Ordering Heuristics for Parallel Graph Coloring
William Hasenplaugh, Tim Kaler, Tao B. Schardl, and Charles E. Leiserson
Partitioning Trillion Edge Graphs
George M. Slota, Karen Devine, Sivasankaran Rajamanickam, Kamesh Madduri
New Phenomena in Large-Scale Internet Traffic
Jeremy 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 Computations
Jessica Shi and Julian Shun
Models
Recent Advances in Scalable Network Generation
Manuel 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 Parallel
Ajitesh Srivastava, Charalampos Chelmis, Viktor K. Prasanna
Executing Dynamic Data-Graph Computations Deterministically Using Chromatic Scheduling
Tim Kaler, William Hasenplaugh, Tao B. Schardl, and Charles E. Leiserson
Frameworks and Software
Graph Data Science Using Neo4j
Amy E. Hodler, Mark Needham
The Parallel Boost Graph Library 2.0
Nicholas Edmonds and Andrew Lumsdaine
RAPIDS cuGraph
Alex Fender, Bradley Rees, Joe Eaton
A Cloud-based approach to Big Graphs
Paul Burkhardt and Christopher A. Waring
Introduction to GraphBLAS
Jeremy 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. Mattson
Graphulo: Linear Algebra Graph Kernels
Vijay Gadepally, Jake Bolewski, Daniel Hook, Shana Hutchison, Benjamin A Miller, Jeremy Kepner
Interactive Graph Analytics at Scale in Arkouda
Zhihui Du, Oliver Alvarado Rodriguez, Joseph Patchett, and David A. Bader
List of Contributors
Introduction
Algorithms: Search and Paths
A 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 Paths
Stephan Erb, Moritz Kobitzsch, Lawrence Mandow , and Peter Sanders
Algorithms: Structure
Multicore Algorithms for Graph Connectivity Problems
George M. Slota, Sivasankaran Rajamanickam, and Kamesh Madduri
Distributed Memory Parallel Algorithms for Massive Graphs
Maksudul Alam, Shaikh Arifuzzaman, Hasanuzzaman Bhuiyan, Maleq Khan, V.S. Anil Kumar, and Madhav Marathe
Efficient Multi-core Algorithms for Computing Spanning Forests and Connected Components
Fredrik Manne, Md. Mostofa Ali Patwary
Massive-Scale Distributed Triangle Computation and Applications
Geoffrey Sanders, Roger Pearce, Benjamin W. Priest, Trevor Steil
Algorithms and Applications
Computing Top-k Closeness Centrality in Fully-dynamic Graphs
Eugenio Angriman, Patrick Bisenius, Elisabetta Bergamini, Henning Meyerhenke
Ordering Heuristics for Parallel Graph Coloring
William Hasenplaugh, Tim Kaler, Tao B. Schardl, and Charles E. Leiserson
Partitioning Trillion Edge Graphs
George M. Slota, Karen Devine, Sivasankaran Rajamanickam, Kamesh Madduri
New Phenomena in Large-Scale Internet Traffic
Jeremy 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 Computations
Jessica Shi and Julian Shun
Models
Recent Advances in Scalable Network Generation
Manuel 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 Parallel
Ajitesh Srivastava, Charalampos Chelmis, Viktor K. Prasanna
Executing Dynamic Data-Graph Computations Deterministically Using Chromatic Scheduling
Tim Kaler, William Hasenplaugh, Tao B. Schardl, and Charles E. Leiserson
Frameworks and Software
Graph Data Science Using Neo4j
Amy E. Hodler, Mark Needham
The Parallel Boost Graph Library 2.0
Nicholas Edmonds and Andrew Lumsdaine
RAPIDS cuGraph
Alex Fender, Bradley Rees, Joe Eaton
A Cloud-based approach to Big Graphs
Paul Burkhardt and Christopher A. Waring
Introduction to GraphBLAS
Jeremy 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. Mattson
Graphulo: Linear Algebra Graph Kernels
Vijay Gadepally, Jake Bolewski, Daniel Hook, Shana Hutchison, Benjamin A Miller, Jeremy Kepner
Interactive Graph Analytics at Scale in Arkouda
Zhihui Du, Oliver Alvarado Rodriguez, Joseph Patchett, and David A. Bader