Massive Graph Analytics

by David A. Bader (Editor)

Overview

"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.

Product Details

ISBN-13:	9781000538694
Publisher:	CRC Press
Publication date:	07/20/2022
Series:	Chapman & Hall/CRC Data Science Series
Sold by:	Barnes & Noble
Format:	eBook
Pages:	590
File size:	30 MB
Note:	This product may take a few minutes to download.

About the Author

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.

Table of Contents

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, Peter Michaleas, Lauren Milechin

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

Editorial Reviews

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

From the Publisher