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

TL;DR

This paper introduces PSimGNN, a hierarchical graph matching model that partitions graphs into subgraphs and uses attention-based neural networks to improve similarity computation, especially for large graphs.

Contribution

The paper proposes a novel hierarchical approach combining graph partitioning and neural networks with attention to enhance graph similarity measurement.

Findings

Outperforms state-of-the-art methods on various graph datasets.

Effectively handles large graphs with reduced computation cost.

Achieves higher accuracy in graph similarity tasks using approximate GED.

Abstract

Graph similarity computation aims to predict a similarity score between one pair of graphs to facilitate downstream applications, such as finding the most similar chemical compounds similar to a query compound or Fewshot 3D Action Recognition. 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 reduced representation ability or high computation cost. 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 extract the local structural features directly. Next, a novel graph neural network with an attention mechanism is designed to map each subgraph into an embedding vector. Some of these subgraph pairs are automatically 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. Experimental results on graph datasets with different graph sizes demonstrate that PSimGNN outperforms state-of-the-art methods in graph similarity computation tasks using approximate Graph Edit Distance (GED) as the graph similarity metric.