On efficient network similarity measures

作者：

Highlights：

• The approach is novel and application oriented.

• It outperforms classical graph similarity methods/measures.

• It relates to measure the structural similarity of graphs efficiently.

• The method has immediate applications in engineering, computational biology, chemistry (and other areas).

• We analyzed mathematical properties of the measures and classified them.

摘要

•The approach is novel and application oriented.•It outperforms classical graph similarity methods/measures.•It relates to measure the structural similarity of graphs efficiently.•The method has immediate applications in engineering, computational biology, chemistry (and other areas).•We analyzed mathematical properties of the measures and classified them.

论文关键词：Distance measures,Similarity measures,Inequalities,Graphs,Networks

论文评审过程：Received 13 March 2019, Revised 28 May 2019, Accepted 11 June 2019, Available online 3 July 2019, Version of Record 3 July 2019.

论文官网地址：https://doi.org/10.1016/j.amc.2019.06.035