TY  - GEN
N2  - 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.
DO  - 10.1007/978-981-15-3
AB  - 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.
T1  - Large-scale graph analysis :system, algorithm and optimization /
DA  - 2020.
CY  - Singapore :
AU  - Shao, Yingxia.
AU  - Cui, Bin.
AU  - Chen, Lei.
CN  - QA166.245
PB  - Springer,
PP  - Singapore :
PY  - 2020.
ID  - 938167
KW  - Graph algorithms.
SN  - 9789811539282
SN  - 9811539286
TI  - Large-scale graph analysis :system, algorithm and optimization /
LK  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-15-3928-2
UR  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-15-3928-2
ER  -