"""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."
Edited by:
David A. Bader
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Dimensions:
Height: 254mm,
Width: 178mm,
Weight: 471g
ISBN: 9780367464127
ISBN 10: 0367464128
Series: Chapman & Hall/CRC Data Science Series
Pages: 590
Publication Date: 20 July 2022
Audience:
Professional and scholarly
,
College/higher education
,
Undergraduate
,
Further / Higher Education
Format: Hardback
Publisher's Status: Active
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
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.
Reviews for Massive Graph Analytics
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
