A Structural Feature-Based Approach for Comprehensive Graph Classification

TL;DR

This paper introduces a simple yet effective graph classification method based on fundamental structural features, demonstrating competitive performance and broad applicability across various domains.

Contribution

The paper presents a novel feature-based approach that uses basic graph properties for classification, offering a practical alternative to complex graph learning methods.

Findings

Achieves competitive or superior accuracy compared to state-of-the-art methods.

Demonstrates effectiveness across social networks, bioinformatics, and cybersecurity.

Shows simplicity enhances accessibility and adaptability.

Abstract

The increasing prevalence of graph-structured data across various domains has intensified greater interest in graph classification tasks. While numerous sophisticated graph learning methods have emerged, their complexity often hinders practical implementation. In this article, we address this challenge by proposing a method that constructs feature vectors based on fundamental graph structural properties. We demonstrate that these features, despite their simplicity, are powerful enough to capture the intrinsic characteristics of graphs within the same class. We explore the efficacy of our approach using three distinct machine learning methods, highlighting how our feature-based classification leverages the inherent structural similarities of graphs within the same class to achieve accurate classification. A key advantage of our approach is its simplicity, which makes it accessible and adaptable to a broad range of applications, including social network analysis, bioinformatics, and cybersecurity. Furthermore, we conduct extensive experiments to validate the performance of our method, showing that it not only reveals a competitive performance but in some cases surpasses the accuracy of more complex, state-of-the-art techniques. Our findings suggest that a focus on fundamental graph features can provide a robust and efficient alternative for graph classification, offering significant potential for both research and practical applications.