Large-scale graph analysis : system, algorithm and optimization
著者
書誌事項
Large-scale graph analysis : system, algorithm and optimization
（Big data management）
Springer, c2020
- タイトル別名
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Large scale graph analysis : system, algorithm and optimization
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注記
Includes bibliographical references
内容説明・目次
内容説明
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.
目次
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.
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