@article{938167,
      author = {Shao, Yingxia. and Cui, Bin. and Chen, Lei.},
      url = {http://library.usi.edu/record/938167},
      title = {Large-scale graph analysis : system, algorithm and  optimization /},
      publisher = {Springer,},
      abstract = {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.},
      doi = {https://doi.org/10.1007/978-981-15-3},
      recid = {938167},
      pages = {1 online resource},
      address = {Singapore :},
      year = {2020},
}