Pattern-Based Graph Classification: Comparison of Quality Measures and Importance of Preprocessing

TL;DR

This paper compares 38 quality measures for pattern-based graph classification, highlighting the importance of preprocessing and revealing that some popular measures are suboptimal for classification tasks.

Contribution

It provides a comprehensive theoretical and empirical comparison of quality measures and introduces a clustering-based preprocessing step to improve classification performance.

Findings

Preprocessing reduces pattern set size without sacrificing accuracy.

Some widely used quality measures are not optimal for classification.

Clustering patterns enhances interpretability and efficiency.

Abstract

Graph classification aims to categorize graphs based on their structural and attribute features, with applications in diverse fields such as social network analysis and bioinformatics. Among the methods proposed to solve this task, those relying on patterns (i.e. subgraphs) provide good explainability, as the patterns used for classification can be directly interpreted. To identify meaningful patterns, a standard approach is to use a quality measure, i.e. a function that evaluates the discriminative power of each pattern. However, the literature provides tens of such measures, making it difficult to select the most appropriate for a given application. Only a handful of surveys try to provide some insight by comparing these measures, and none of them specifically focuses on graphs. This typically results in the systematic use of the most widespread measures, without thorough evaluation. To address this issue, we present a comparative analysis of 38 quality measures from the literature. We characterize them theoretically, based on four mathematical properties. We leverage publicly available datasets to constitute a benchmark, and propose a method to elaborate a gold standard ranking of the patterns. We exploit these resources to perform an empirical comparison of the measures, both in terms of pattern ranking and classification performance. Moreover, we propose a clustering-based preprocessing step, which groups patterns appearing in the same graphs to enhance classification performance. Our experimental results demonstrate the effectiveness of this step, reducing the number of patterns to be processed while achieving comparable performance. Additionally, we show that some popular measures widely used in the literature are not associated with the best results.