GRETEL 2.0: Generation and Evaluation of Graph Counterfactual Explanations Evolved

The lack of transparency of graph neural networks (GNNs) poses challenges in understanding the results of e.g., friendship prediction, drug discovery, and community detection. Graph Counterfactual Explanation (GCE) techniques aim to enhance interpretability by generating counterfactual examples, improving trustworthiness, and reducing biases in GNN predictions. However, existing literature on GCE lacks standardization in definitions, methodologies, datasets, and evaluation criteria. To address this, we introduced GRETEL, a comprehensive framework for developing and evaluating GCE methods. GRETEL offers fully extensible built-in components, enabling users to define ad-hoc explanation techniques, generate synthetic datasets, implement customized evaluation metrics, and integrate seamlessly with state-of-the-art prediction models. In this demo, we present GRETEL-2, an enhanced version with a focus on usability and extensibility. We illustrate how these features enhance the interpretability and practicality of GNNs across various scenarios.