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Cohesive subgraph computation over l...

Chang, Lijun.

Cohesive subgraph computation over large sparse graphsalgorithms, data structures, and programming techniques /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Cohesive subgraph computation over large sparse graphsby Lijun Chang, Lu Qin.
Reminder of title:	algorithms, data structures, and programming techniques /
Author:	Chang, Lijun.
other author:	Qin, Lu.
Published:	Cham :Springer International Publishing :2018.
Description:	xii, 107 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Graph theory.
Online resource:	https://doi.org/10.1007/978-3-030-03599-0
ISBN:	9783030035990$q(electronic bk.)

Cohesive subgraph computation over large sparse graphsalgorithms, data structures, and programming techniques /
Chang, Lijun.

Cohesive subgraph computation over large sparse graphsalgorithms, data structures, and programming techniques /[electronic resource] :by Lijun Chang, Lu Qin. - Cham :Springer International Publishing :2018. - xii, 107 p. :ill. (some col.), digital ;24 cm. - Springer series in the data sciences,2365-5674. - Springer series in the data sciences..

Introduction -- Linear Heap Data Structures -- Minimum Degree-based Core Decomposition -- Average Degree-based Densest Subgraph Computation -- Higher-order Structure-based Graph Decomposition -- Edge Connectivity-based Graph Decomposition.

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.

ISBN: 9783030035990$q(electronic bk.)

Standard No.: 10.1007/978-3-030-03599-0doiSubjects--Topical Terms:

181880
Graph theory.

LC Class. No.: QA166 / .C436 2018

Dewey Class. No.: 511.5

Cohesive subgraph computation over large sparse graphsalgorithms, data structures, and programming techniques /
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245 1 0 $a Cohesive subgraph computation over large sparse graphs $h [electronic resource] : $b algorithms, data structures, and programming techniques / $c by Lijun Chang, Lu Qin.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xii, 107 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer series in the data sciences, $x 2365-5674
505 0 $a Introduction -- Linear Heap Data Structures -- Minimum Degree-based Core Decomposition -- Average Degree-based Densest Subgraph Computation -- Higher-order Structure-based Graph Decomposition -- Edge Connectivity-based Graph Decomposition.
520 $a 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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650 2 4 $a Data Structures. $3 273992
700 1 $a Qin, Lu. $3 825951
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950 $a Mathematics and Statistics (Springer-11649)