Towards Causal Classification: A Comprehensive Study on Graph Neural Networks

TL;DR

This paper investigates how incorporating causality into Graph Neural Networks (GNNs) affects their performance in graph classification tasks across multiple datasets, aiming to enhance their practical utility.

Contribution

It provides a comprehensive evaluation of nine GNN models across diverse datasets to understand the impact of causality on their effectiveness and flexibility.

Findings

Causality influences GNN performance variably across datasets

Certain models show improved accuracy with causal enhancements

The study identifies gaps for future causal GNN development

Abstract

The exploration of Graph Neural Networks (GNNs) for processing graph-structured data has expanded, particularly their potential for causal analysis due to their universal approximation capabilities. Anticipated to significantly enhance common graph-based tasks such as classification and prediction, the development of a causally enhanced GNN framework is yet to be thoroughly investigated. Addressing this shortfall, our study delves into nine benchmark graph classification models, testing their strength and versatility across seven datasets spanning three varied domains to discern the impact of causality on the predictive prowess of GNNs. This research offers a detailed assessment of these models, shedding light on their efficiency, and flexibility in different data environments, and highlighting areas needing advancement. Our findings are instrumental in furthering the understanding and practical application of GNNs in diverse datacentric fields