Learning graph edit distance by graph neural networks

作者：

Highlights：

• We adapt a traditional non-learnable GED algorithm to the novel paradigm of geometric deep learning.

• Triplet network for learning graph distances by means of graph neural networks.

• Learning distance in the graph domain without an embedding stage.

• Graph-based keyword spotting application with state-of-the-art performance.

摘要

•We adapt a traditional non-learnable GED algorithm to the novel paradigm of geometric deep learning.•Triplet network for learning graph distances by means of graph neural networks.•Learning distance in the graph domain without an embedding stage.•Graph-based keyword spotting application with state-of-the-art performance.

论文关键词：Graph neural networks,Graph edit distance,Geometric deep learning,Keyword spotting,Document image analysis

论文评审过程：Received 3 August 2020, Revised 19 April 2021, Accepted 23 June 2021, Available online 2 July 2021, Version of Record 24 July 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108132