A Simple Baseline Algorithm for Graph Classification

TL;DR

This paper introduces a simple, fast spectral decomposition-based algorithm for graph classification that provides competitive results with less computational complexity, serving as a baseline for future research.

Contribution

The paper presents a novel, straightforward spectral method for graph classification that is computationally efficient and competitive with more complex state-of-the-art approaches.

Findings

Achieves competitive accuracy with existing methods

Requires less computational power

Provides a reliable baseline for graph classification

Abstract

Graph classification has recently received a lot of attention from various fields of machine learning e.g. kernel methods, sequential modeling or graph embedding. All these approaches offer promising results with different respective strengths and weaknesses. However, most of them rely on complex mathematics and require heavy computational power to achieve their best performance. We propose a simple and fast algorithm based on the spectral decomposition of graph Laplacian to perform graph classification and get a first reference score for a dataset. We show that this method obtains competitive results compared to state-of-the-art algorithms.