Generalized Learning of Coefficients in Spectral Graph Convolutional Networks

TL;DR

This paper introduces G-Arnoldi-GCN, a new algorithm for approximating filter functions in spectral graph convolutional networks, improving performance in node classification tasks across diverse datasets.

Contribution

It proposes a novel Arnoldi orthonormalization-based algorithm for efficient polynomial approximation of filter functions in spectral GCNs, addressing ill-conditioning issues.

Findings

G-Arnoldi-GCN outperforms state-of-the-art methods on multiple datasets.

The approach enables explicit design of diverse filter functions.

It demonstrates robustness across various graph topologies.

Abstract

Spectral Graph Convolutional Networks (GCNs) have gained popularity in graph machine learning applications due, in part, to their flexibility in specification of network propagation rules. These propagation rules are often constructed as polynomial filters whose coefficients are learned using label information during training. In contrast to learned polynomial filters, explicit filter functions are useful in capturing relationships between network topology and distribution of labels across the network. A number of algorithms incorporating either approach have been proposed; however the relationship between filter functions and polynomial approximations is not fully resolved. This is largely due to the ill-conditioned nature of the linear systems that must be solved to derive polynomial approximations of filter functions. To address this challenge, we propose a novel Arnoldi orthonormalization-based algorithm, along with a unifying approach, called G-Arnoldi-GCN that can efficiently and effectively approximate a given filter function with a polynomial. We evaluate G-Arnoldi-GCN in the context of multi-class node classification across ten datasets with diverse topological characteristics. Our experiments show that G-Arnoldi-GCN consistently outperforms state-of-the-art methods when suitable filter functions are employed. Overall, G-Arnoldi-GCN opens important new directions in graph machine learning by enabling the explicit design and application of diverse filter functions. Code link: https://github.com/mustafaCoskunAgu/GArnoldi-GCN