Details

Autor(en) / Beteiligte

• Shao, Yingxia
• Cui, Bin
• Chen, Lei

Titel

Large-scale Graph Analysis: System, Algorithm and Optimization [electronic resource]

Auflage

1st ed. 2020

Ort / Verlag

Singapore : Springer Singapore

Erscheinungsjahr

2020

Link zum Volltext

• SpringerLink Books - AutoHoldings

Beschreibungen/Notizen

• Includes bibliographical references.
• 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.
• 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.