Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernels

TL;DR

This paper introduces a method to learn the importance weights of subtree patterns in Weisfeiler-Lehman graph kernels, improving graph classification performance by tailoring substructure significance.

Contribution

It proposes a novel approach to learn subtree pattern weights within WL kernels, along with an efficient algorithm and theoretical convergence guarantees.

Findings

Improved graph classification accuracy on synthetic and real-world datasets.

Efficient learning algorithm with proven convergence.

Demonstrated the effectiveness of weighted subtree patterns in kernels.

Abstract

Graph is an usual representation of relational data, which are ubiquitous in manydomains such as molecules, biological and social networks. A popular approach to learningwith graph structured data is to make use of graph kernels, which measure the similaritybetween graphs and are plugged into a kernel machine such as a support vector machine.Weisfeiler-Lehman (WL) based graph kernels, which employ WL labeling scheme to extract subtree patterns and perform node embedding, are demonstrated to achieve great performance while being efficiently computable. However, one of the main drawbacks of ageneral kernel is the decoupling of kernel construction and learning process. For moleculargraphs, usual kernels such as WL subtree, based on substructures of the molecules, consider all available substructures having the same importance, which might not be suitable inpractice. In this paper, we propose a method to learn the weights of subtree patterns in the framework of WWL kernels, the state of the art method for graph classification task [14]. To overcome the computational issue on large scale data sets, we present an efficient learning algorithm and also derive a generalization gap bound to show its convergence. Finally, through experiments on synthetic and real-world data sets, we demonstrate the effectiveness of our proposed method for learning the weights of subtree patterns.