Large-scale graph analysis : system, algorithm and optimization / Yingxia Shao, Bin Cui, Lei Chen.
2020
QA166.245
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Online Access
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Unlimited
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Authorized users
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Can lend chapters, not whole ebooks
Details
Title
Large-scale graph analysis : system, algorithm and optimization / Yingxia Shao, Bin Cui, Lei Chen.
Author
Shao, Yingxia.
ISBN
9789811539282 (electronic book)
9811539286 (electronic book)
9811539278
9789811539275
9811539286 (electronic book)
9811539278
9789811539275
Imprint
Singapore : Springer, 2020.
Language
English
Description
1 online resource
Other Standard Identifiers
10.1007/978-981-15-3
Call Number
QA166.245
Dewey Decimal Classification
518/.1
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Added Author
Cui, Bin.
Chen, Lei.
Chen, Lei.
Series
Big data management.
Available in Other Form
Print version: 9789811539275
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Online Access
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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.
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.