Deep graph similarity learning: a survey

Guixiang Ma,Theodore L Willke,Philip S Yu,Nesreen K Ahmed

doi:10.1007/s10618-020-00733-5

Guixiang Ma, Theodore L Willke + Show 2 more

Open Access

https://doi.org/10.1007/s10618-020-00733-5

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Abstract

In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search. Recently, there has been an increasing interest in deep graph similarity learning, where the key idea is to learn a deep learning model that maps input graphs to a target space such that the distance in the target space approximates the structural distance in the input space. Here, we provide a comprehensive review of the existing literature of deep graph similarity learning. We propose a systematic taxonomy for the methods and applications. Finally, we discuss the challenges and future directions for this problem.

Highlights

Learning an adequate similarity measure on a feature space can significantly determine the performance of machine learning methods
Based on the different graph representation learning strategies and how they are leveraged for the deep graph similarity learning task, we propose to categorize deep graph similarity learning models into three groups: Graph Embedding based-methods, graph neural networks (GNNs)-based methods, and Deep Graph Kernel-based methods
The problem of graph matching is different from the graph similarity learning problem we focus on in this survey and is beyond the scope of this survey, some work on deep graph matching networks involves graph similarity learning and we review some of this work below to provide some insights into how deep similarity learning may be leveraged for graph matching applications, such as image matching

Summary

Introduction

Learning an adequate similarity measure on a feature space can significantly determine the performance of machine learning methods. Learning such measures automatically from data is the primary aim of similarity learning. Similarity/Metric learning refers to learning a function to measure the distance or similarity between objects, which is a critical step in many machine learning problems, such as classification, clustering, ranking, etc. There are some general metrics like Euclidean distance that can be used for getting similarity measure between objects represented as vectors, these metrics often fail to capture the specific characteristics of the data being studied, especially for structured data. It is essential to find or learn a metric for measuring the similarity of data points involved in the specific task

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