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Cohesive Subgraph Computation over Large Sparse Graphs

Algorithms, Data Structures, and Programming Techniques

  • Book
  • © 2018

Overview

Authors:
  1. Lijun Chang

    1. School of Computer Science, The University of Sydney, Sydney, Australia

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Lu Qin

    1. Centre for Artificial Intelligence, University of Technology Sydney, Sydney, Australia

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Includes data structures that can be of general use for efficient graph processing
  • Considered the first extended survey on algorithms and techniques for efficient cohesive subgraph computation
  • Source code of highly optimized algorithms is provided

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

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xii
    Download chapter PDF

  2. Introduction

    • Lijun Chang, Lu Qin
    Pages 1-8

  3. Linear Heap Data Structures

    • Lijun Chang, Lu Qin
    Pages 9-20

  4. Minimum Degree-Based Core Decomposition

    • Lijun Chang, Lu Qin
    Pages 21-39

  5. Average Degree-Based Densest Subgraph Computation

    • Lijun Chang, Lu Qin
    Pages 41-53

  6. Higher-Order Structure-Based Graph Decomposition

    • Lijun Chang, Lu Qin
    Pages 55-75

  7. Edge Connectivity-Based Graph Decomposition

    • Lijun Chang, Lu Qin
    Pages 77-98

  8. Back Matter

    Pages 99-107
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Keywords

About this book

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.


Authors and Affiliations

  • School of Computer Science, The University of Sydney, Sydney, Australia

    Lijun Chang

  • Centre for Artificial Intelligence, University of Technology Sydney, Sydney, Australia

    Lu Qin

Bibliographic Information

  • Book Title: Cohesive Subgraph Computation over Large Sparse Graphs

  • Book Subtitle: Algorithms, Data Structures, and Programming Techniques

  • Authors: Lijun Chang, Lu Qin

  • Series Title: Springer Series in the Data Sciences

  • DOI: https://doi.org/10.1007/978-3-030-03599-0

  • Publisher: Springer Cham

  • eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)

  • Copyright Information: Springer Nature Switzerland AG 2018

  • Hardcover ISBN: 978-3-030-03598-3Published: 07 January 2019

  • eBook ISBN: 978-3-030-03599-0Published: 24 December 2018

  • Series ISSN: 2365-5674

  • Series E-ISSN: 2365-5682

  • Edition Number: 1

  • Number of Pages: XII, 107

  • Number of Illustrations: 20 b/w illustrations, 1 illustrations in colour

  • Topics: Algorithms, Data Structures

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