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Cohesive Subgraph Computation over Large Sparse Graphs

Algorithms, Data Structures, and Programming Techniques

  • Lijun Chang
  • Lu Qin

Part of the Springer Series in the Data Sciences book series (SSDS)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Lijun Chang, Lu Qin
    Pages 1-8
  3. Lijun Chang, Lu Qin
    Pages 9-20
  4. Lijun Chang, Lu Qin
    Pages 21-39
  5. Lijun Chang, Lu Qin
    Pages 41-53
  6. Lijun Chang, Lu Qin
    Pages 55-75
  7. Lijun Chang, Lu Qin
    Pages 77-98
  8. Back Matter
    Pages 99-107

About this book

Introduction

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.

Keywords

Cohesive Subgraph Computation K-Core Densest Subgraph K-Edge Connected Component Maximum Clique

Authors and affiliations

  • Lijun Chang
    • 1
  • Lu Qin
    • 2
  1. 1.School of Computer ScienceThe University of SydneySydneyAustralia
  2. 2.Centre for Artificial IntelligenceUniversity of Technology SydneySydneyAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-03599-0
  • Copyright Information Springer Nature Switzerland AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-03598-3
  • Online ISBN 978-3-030-03599-0
  • Series Print ISSN 2365-5674
  • Series Online ISSN 2365-5682
  • Buy this book on publisher's site