Large-scale Graph Analysis: System, Algorithm and Optimization
Shao, Yingxia.
Large-scale Graph Analysis: System, Algorithm and Optimization [electronic resource] / by Yingxia Shao, Bin Cui, Lei Chen. - 1st ed. 2020. - XIII, 146 p. 78 illus., 30 illus. in color. online resource. - Big Data Management, 2522-0187 . - 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 fordesigning efficient large-scale graph algorithms.
9789811539282
10.1007/978-981-15-3928-2 doi
Big data.
Data mining.
Graph theory.
Electronic data processing--Management.
Big Data.
Data Mining and Knowledge Discovery.
Graph Theory.
IT Operations.
QA76.9.B45
005.7
Large-scale Graph Analysis: System, Algorithm and Optimization [electronic resource] / by Yingxia Shao, Bin Cui, Lei Chen. - 1st ed. 2020. - XIII, 146 p. 78 illus., 30 illus. in color. online resource. - Big Data Management, 2522-0187 . - 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 fordesigning efficient large-scale graph algorithms.
9789811539282
10.1007/978-981-15-3928-2 doi
Big data.
Data mining.
Graph theory.
Electronic data processing--Management.
Big Data.
Data Mining and Knowledge Discovery.
Graph Theory.
IT Operations.
QA76.9.B45
005.7