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Large-scale graph analysis = system,...

Shao, Yingxia.

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Large-scale graph analysis = system, algorithm and optimization /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Large-scale graph analysis/ by Yingxia Shao, Bin Cui, Lei Chen.
其他題名:	system, algorithm and optimization /
作者:	Shao, Yingxia.
其他作者:	Cui, Bin.
出版者:	Singapore :Springer Singapore : : 2020.,
面頁冊數:	xiii, 146 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Graph algorithms. -
電子資源:	https://doi.org/10.1007/978-981-15-3928-2
ISBN:	9789811539282

Large-scale graph analysis = system, algorithm and optimization /
Shao, Yingxia.

Large-scale graph analysissystem, algorithm and optimization /[electronic resource] :by Yingxia Shao, Bin Cui, Lei Chen. - Singapore :Springer Singapore :2020. - xiii, 146 p. :ill., digital ;24 cm. - Big data management,2522-0179. - Big data management..

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.

ISBN: 9789811539282

Standard No.: 10.1007/978-981-15-3928-2doiSubjects--Topical Terms:

596326
Graph algorithms.

LC Class. No.: QA166.245 / .S43 2020

Dewey Class. No.: 518.1

Large-scale graph analysis = system, algorithm and optimization /
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505 0 $a 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.
520 $a 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.
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