Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation

by   Haoyan Xu, et al.

Graph similarity computation aims to predict a similarity score between one pair of graphs so as to facilitate downstream applications, such as finding the chemical compounds that are most similar to a query compound or Fewshot 3D Action Recognition, etc. Recently, some graph similarity computation models based on neural networks have been proposed, which are either based on graph-level interaction or node-level comparison. However, when the number of nodes in the graph increases, it will inevitably bring about the problem of reduced representation ability or excessive time complexity. Motivated by this observation, we propose a graph partitioning and graph neural network based model, called PSimGNN, to effectively resolve this issue. Specifically, each of the input graphs is partitioned into a set of subgraphs to directly extract the local structural features firstly. Next, a learnable embedding function is used to map each subgraph into an embedding vector. Then, some of these subgraph pairs are selected for node-level comparison to supplement the subgraph-level embedding with fine-grained information. Finally, coarse-grained interaction information among subgraphs and fine-grained comparison information among nodes in different subgraphs are integrated to predict the final similarity score. Using approximate Graph Edit Distance (GED) as graph similarity metric, experimental results on graph data sets of different graph size demonstrate PSimGNN outperforms state-of-the-art methods in graph similarity computation tasks. The codes will release when this paper is published.


page 1

page 2

page 3

page 4


Graph Edit Distance Computation via Graph Neural Networks

Graph similarity search is among the most important graph-based applicat...

Hierarchical Graph Matching Networks for Deep Graph Similarity Learning

While the celebrated graph neural networks yield effective representatio...

Graph Edit Distance Learning via Different Attention

Recently, more and more research has focused on using Graph Neural Netwo...

Fast Detection of Maximum Common Subgraph via Deep Q-Learning

Detecting the Maximum Common Subgraph (MCS) between two input graphs is ...

A Neural Framework for Learning Subgraph and Graph Similarity Measures

Subgraph similarity search is a fundamental operator in graph analysis. ...

TrQuery: An Embedding-based Framework for Recommanding SPARQL Queries

In this paper, we present an embedding-based framework (TrQuery) for rec...

More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference

Graph similarity measurement, which computes the distance/similarity bet...

Please sign up or login with your details

Forgot password? Click here to reset