MR-SimLab: Scalable subgraph selection with label similarity for big data

作者：

Highlights：

• Existing feature selection algorithms are facing a scalability challenge.

• We propose MR-SimLab, a MapReduce-based subgraph selection approach for big data.

• MR-SimLab leverages label similarity to perform an approximate subgraph matching.

• MR-SimLab algorithm is distributed, scalable, and selects informative subgraphs.

摘要

•Existing feature selection algorithms are facing a scalability challenge.•We propose MR-SimLab, a MapReduce-based subgraph selection approach for big data.•MR-SimLab leverages label similarity to perform an approximate subgraph matching.•MR-SimLab algorithm is distributed, scalable, and selects informative subgraphs.

论文关键词：Feature selection,Subgraph mining,Label similarity,MapReduce

论文评审过程：Received 6 September 2016, Revised 16 May 2017, Accepted 22 May 2017, Available online 25 May 2017, Version of Record 30 May 2017.

论文官网地址：https://doi.org/10.1016/j.is.2017.05.006