Massive Graph Analytics (eBook, PDF)

Redaktion: Bader, David A.

• Format: PDF

Produktbeschreibung

"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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 616
• Erscheinungstermin: 20. Juli 2022
• Englisch
• ISBN-13: 9781000538618
• Artikelnr.: 64097397

Autorenporträt

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.

Inhaltsangabe

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