Cohesive Subgraph Computation over Large Sparse Graphs

Algorithms, Data Structures, and Programming Techniques

Nonfiction, Computers, Advanced Computing, Programming, Data Modeling & Design, Science & Nature, Mathematics
Cover of the book Cohesive Subgraph Computation over Large Sparse Graphs by Lijun Chang, Lu Qin, Springer International Publishing
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Author: Lijun Chang, Lu Qin ISBN: 9783030035990
Publisher: Springer International Publishing Publication: December 24, 2018
Imprint: Springer Language: English
Author: Lijun Chang, Lu Qin
ISBN: 9783030035990
Publisher: Springer International Publishing
Publication: December 24, 2018
Imprint: Springer
Language: English

This book is considered the first extended survey on algorithms and techniques for efficient cohesive subgraph computation. With rapid development of information technology, huge volumes of graph data are accumulated. An availability of rich graph data not only brings great opportunities for realizing big values of data to serve key applications, but also brings great challenges in computation. Using a consistent terminology, the book gives an excellent introduction to the models and algorithms for the problem of cohesive subgraph computation. The materials of this book are well organized from introductory content to more advanced topics while also providing well-designed source codes for most algorithms described in the book.

 

This is a timely book for researchers who are interested in this topic and efficient data structure design for large sparse graph processing. It is also a guideline book for new researchers to get to know the area of cohesive subgraph computation.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book is considered the first extended survey on algorithms and techniques for efficient cohesive subgraph computation. With rapid development of information technology, huge volumes of graph data are accumulated. An availability of rich graph data not only brings great opportunities for realizing big values of data to serve key applications, but also brings great challenges in computation. Using a consistent terminology, the book gives an excellent introduction to the models and algorithms for the problem of cohesive subgraph computation. The materials of this book are well organized from introductory content to more advanced topics while also providing well-designed source codes for most algorithms described in the book.

 

This is a timely book for researchers who are interested in this topic and efficient data structure design for large sparse graph processing. It is also a guideline book for new researchers to get to know the area of cohesive subgraph computation.

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