GRETEL: A unified framework for Graph Counterfactual Explanation Evaluation
Mario Alfonso Prado-Romero, Giovanni Stilo

TL;DR
GRETEL is a comprehensive, extensible framework designed to evaluate Graph Counterfactual Explanations across various datasets, models, and explanation methods, promoting reproducibility and standardization in the field.
Contribution
It introduces GRETEL, a unified, extensible evaluation framework for Graph Counterfactual Explanations, enabling consistent comparison and fostering open science practices.
Findings
GRETEL successfully integrates multiple datasets and explanation techniques.
The framework facilitates reproducible and standardized evaluation of GCE methods.
Experiments demonstrate GRETEL's effectiveness in benchmarking diverse GCE approaches.
Abstract
Machine Learning (ML) systems are a building part of the modern tools which impact our daily life in several application domains. Due to their black-box nature, those systems are hardly adopted in application domains (e.g. health, finance) where understanding the decision process is of paramount importance. Explanation methods were developed to explain how the ML model has taken a specific decision for a given case/instance. Graph Counterfactual Explanations (GCE) is one of the explanation techniques adopted in the Graph Learning domain. The existing works of Graph Counterfactual Explanations diverge mostly in the problem definition, application domain, test data, and evaluation metrics, and most existing works do not compare exhaustively against other counterfactual explanation techniques present in the literature. We present GRETEL, a unified framework to develop and test GCE methods…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Advanced Graph Neural Networks · Machine Learning in Healthcare
MethodsBalanced Selection
