Large-scale Graph Analysis: System, Algorithm and Optimization

  • Yingxia Shao
  • Bin Cui
  • Lei Chen

Part of the Big Data Management book series (BIGDM)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Yingxia Shao, Bin Cui, Lei Chen
    Pages 1-13
  3. Yingxia Shao, Bin Cui, Lei Chen
    Pages 15-29
  4. Yingxia Shao, Bin Cui, Lei Chen
    Pages 31-55
  5. Yingxia Shao, Bin Cui, Lei Chen
    Pages 57-86
  6. Yingxia Shao, Bin Cui, Lei Chen
    Pages 87-114
  7. Yingxia Shao, Bin Cui, Lei Chen
    Pages 115-144
  8. Yingxia Shao, Bin Cui, Lei Chen
    Pages 145-146

About this book


This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms – the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms.

This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology for designing efficient large-scale graph algorithms.


Large-scale graph analysis Graph algorithm optimization Distributed graph computing Parallel graph computing Subgraph enumeration Graph matching Cohesive subgraph detection Graph partition-aware system

Authors and affiliations

  1. 1.School of Computer ScienceBeijing University of Posts and Telecommunications BeijingBeijingChina
  2. 2.School of Electronics Engineering and Computer SciencePeking University BeijingBeijingChina
  3. 3.Department of Computer Science and EngineeringHong Kong University of Science and TechnologyHong KongChina

Bibliographic information

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