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Large-scale graph analysis = system,...

Shao, Yingxia.

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Large-scale graph analysis/ by Yingxia Shao, Bin Cui, Lei Chen.
Reminder of title:	system, algorithm and optimization /
Author:	Shao, Yingxia.
other author:	Cui, Bin.
Published:	Singapore :Springer Singapore : : 2020.,
Description:	xiii, 146 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	Graph algorithms. -
Online resource:	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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245 1 0 $a Large-scale graph analysis $h [electronic resource] : $b system, algorithm and optimization / $c by Yingxia Shao, Bin Cui, Lei Chen.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a xiii, 146 p. : $b ill., digital ; $c 24 cm.
490 1 $a Big data management, $x 2522-0179
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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650 1 4 $a Big Data. $3 3134868
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Applications of Graph Theory and Complex Networks. $3 3134760
650 2 4 $a Management of Computing and Information Systems. $3 892490
700 1 $a Cui, Bin. $3 2194857
700 1 $a Chen, Lei. $3 1085552
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