Wow! eBook
Massive Graph Analytics
Author(s): David A. Bader
- Publisher: Chapman and Hall/CRC
- Publication Date: 20 July 2022
- Edition: 1st
- Language: English
- Print length: 590 pages
- ISBN-10: 0367464128
- ISBN-13: 9780367464127
Book 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 bookprovides 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.
Editorial Reviews
Review
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
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.
