A dynamic sampling algorithm based on learning automata for stochastic trust networks

作者：

Highlights：

• A stochastic graph model for representing trust networks is recommended.

• We present a comprehensive review of the literature on analyzing complex networks.

• New extensions of some graph measures are proposed, considering properties of trust.

• We develop a dynamic sampling algorithm for stochastic trust networks.

• The experimental results show the benefits of our proposed sampling algorithm.

摘要

•A stochastic graph model for representing trust networks is recommended.•We present a comprehensive review of the literature on analyzing complex networks.•New extensions of some graph measures are proposed, considering properties of trust.•We develop a dynamic sampling algorithm for stochastic trust networks.•The experimental results show the benefits of our proposed sampling algorithm.

论文关键词：Trust networks,Stochastic graphs,Graph measures,Network sampling,Learning automata

论文评审过程：Received 13 June 2020, Revised 2 November 2020, Accepted 21 November 2020, Available online 26 November 2020, Version of Record 1 December 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2020.106620