Using entropy metrics for pruning very large graph cubes

作者：

Highlights：

• Propose entropy metrics for evaluating interactions within large graph datasets.

• Propose techniques to weigh possible OLAP drill-down operations on graph cubes.

• Present an efficient algorithm for fast selection of aggregated sub-graphs.

• Evaluation of the presented techniques in large real and synthetic graph datasets.

摘要

•Propose entropy metrics for evaluating interactions within large graph datasets.•Propose techniques to weigh possible OLAP drill-down operations on graph cubes.•Present an efficient algorithm for fast selection of aggregated sub-graphs.•Evaluation of the presented techniques in large real and synthetic graph datasets.

论文关键词：Graph cube,Entropy,Big data

论文评审过程：Received 4 December 2017, Revised 25 September 2018, Accepted 19 November 2018, Available online 22 November 2018, Version of Record 27 November 2018.

论文官网地址：https://doi.org/10.1016/j.is.2018.11.007