大規模グラフ解析 Large-scale Graph Analysis: System, Algorithm and Optimization

著者名：Shao, Yingxia/Cui, Bin/Chen, Lei

Springer（2020/07/01発売）

• 言語：ENG
• ISBN：9789811539275
• eISBN：9789811539282

Description

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 fordesigning efficient large-scale graph algorithms.

Table of Contents

1. Introduction.- 2. Graph Computing Systems for Large-Scale Graph Analysis.- 3. Partition-Aware Graph Computing System.- 4. Efficient Parallel Subgraph Enumeration.- 5. Efficient Parallel Graph Extraction.- 6. Efficient Parallel Cohesive Subgraph Detection.- 7. Conclusions.