Cohesive Subgraph Computation over Large Sparse Graphs: Algorithms, Data Structures, and Programming Techniques

✍ Scribed by Lijun Chang, Lu Qin

• Publisher: Springer International Publishing
• Year: 2018
• Tongue: English
• Leaves: 113
• Series: Springer Series in the Data Sciences
• Edition: 1st ed.
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xii
Introduction (Lijun Chang, Lu Qin)....Pages 1-8
Linear Heap Data Structures (Lijun Chang, Lu Qin)....Pages 9-20
Minimum Degree-Based Core Decomposition (Lijun Chang, Lu Qin)....Pages 21-39
Average Degree-Based Densest Subgraph Computation (Lijun Chang, Lu Qin)....Pages 41-53
Higher-Order Structure-Based Graph Decomposition (Lijun Chang, Lu Qin)....Pages 55-75
Edge Connectivity-Based Graph Decomposition (Lijun Chang, Lu Qin)....Pages 77-98
Back Matter ....Pages 99-107

✦ Subjects

Mathematics; Algorithms; Data Structures