Jun 7, 2021 · We propose a graph partitioning and graph neural network-based model, called PSimGNN, to effectively resolve this issue.

May 16, 2020 · We propose a graph partitioning and graph neural network-based model, called PSimGNN, to effectively resolve this issue.

Jun 7, 2021 · We propose a graph partitioning and graph neural network-based model, called PSimGNN, to effectively resolve this issue.

Recently, some graph similarity computation models based on neural networks have been proposed, which are either based on graph-level interaction or node-level.

Graph similarity computation aims to predict a similarity score between one pair of graphs to facilitate downstream applications, such as finding the most ...

May 16, 2020 · Graph similarity computation aims to predict a similarity score between one pair of graphs so as to facilitate downstream applications, ...

May 16, 2020 · This work establishes a theoretical connection between graph partitioning and graph isomorphism, and introduces a novel GNN architecture, namely Graph ...

Graph partitioning and graph neural network based hierarchical graph matching for graph similarity computation ... Authors: Haoyan Xu; Ziheng Duan; Yueyang Wang ...

Graph similarity computation aims to predict a similarity score between one pair of graphs so as to facilitate downstream applications, ...

Jan 26, 2021 · Graph similarity computation aims to predict a similarity score between one pair of graphs to facilitate downstream applications, ...