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Massive Graph Analytics

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Address Space
Adjacency Lists
Adjacency Matrix
BFS Tree
Biconnected Component
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chromatic scheduling
cloud-based analytics
Compute Nodes
Connected Components
CUDA Toolkit
Dag Model
data science
Data Sets
Degree Sequence
distributed systems
dynamic graph computation techniques
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Execution Time
Graph Algorithms
graph theory
High Performance Computing
Incidence Matrix
internet traffic datasets
Massive Graphs
multicore processing
Parallel Algorithms
parallel computing methods
Prefix Sum
Priority Queue
Random Graphs
Real World Graphs
Runtime System
scalable network analysis
Sequential Algorithm
supercomputing
Triangle Counting
Undirected Graph

Product details

  • ISBN 9780367464127
  • Weight: 1300g
  • Dimensions: 178 x 254mm
  • Publication Date: 20 Jul 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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.
About

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.