A distributed model for sampling large scale social networks

作者：

Highlights：

• We investigate the effects of graph sampling on reducing the graph’s size.

• We produce an approximate representation that retains the graph’s structure.

• Our distributed method DGS is based on the MapReduce paradigm.

• DGS is able to cope with large scale social networks.

• For real world social networks our model demonstrates its efficiency.

摘要

•We investigate the effects of graph sampling on reducing the graph’s size.•We produce an approximate representation that retains the graph’s structure.•Our distributed method DGS is based on the MapReduce paradigm.•DGS is able to cope with large scale social networks.•For real world social networks our model demonstrates its efficiency.

论文关键词：Social networks,Graph sampling,MapReduce paradigm,Degree centrality

论文评审过程：Received 11 February 2021, Revised 14 June 2021, Accepted 13 August 2021, Available online 22 August 2021, Version of Record 27 August 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115773