graph learning

Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networks
Graph Neural Networks (GNNs) are increasingly adopted across domains such as molecular biology and social network analysis, yet their …
Matteo De Sanctis, Riccardo De Sanctis, Stefano Faralli, Paola Velardi, Bardh Prenkaj
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Exploring Ensemble Strategies for Graph Counterfactual Explanations
Recent advancements in graph neural networks (GNNs) have significantly enhanced the performance of AI systems in tasks such as …
Mario Alfonso Prado-Romero, Bardh Prenkaj, Giovanni Stilo
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Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
Large Language Models (LLMs) have shown strong potential for tabular data generation by modeling textualized feature-value pairs. …
Zheyu Zhang, Shuo Yang, Bardh Prenkaj, Gjergji Kasneci
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Graph Inverse Style Transfer for Counterfactual Explainability
Counterfactual explainability seeks to uncover model decisions by identifying minimal changes to the input that alter the predicted …
Bardh Prenkaj, Efstratios Zaradoukas, Gjergji Kasneci
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GRETEL 2.0: Generation and Evaluation of Graph Counterfactual Explanations Evolved
Mario Alfonso Prado-Romero, Bardh Prenkaj, Giovanni Stilo
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Unifying Evolution, Explanation, and Discernment: A Generative Approach for Dynamic Graph Counterfactuals
We present GRACIE (Graph Recalibration and Adaptive Counterfactual Inspection and Explanation), a novel approach for generative …
Bardh Prenkaj, Mario Villaizan-Vallelado, Tobias Leemann, Gjergji Kasneci
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Robust Stochastic Graph Generator for Counterfactual Explanations
Counterfactual Explanation (CE) techniques have garnered attention as a means to provide insights to the users engaging with AI …
Mario Alfonso Prado-Romero, Bardh Prenkaj, Giovanni Stilo
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Robust Stochastic Graph Generator for Counterfactual Explanations
Are Generative-Based Graph Counterfactual Explainers Worth It?
Mario Alfonso Prado-Romero, Bardh Prenkaj, Giovanni Stilo
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Revisiting CounteRGAN for Counterfactual Explainability of Graphs
Counterfactual explainability (CE) has been widely explored in various domains ranging from medical image diagnosis to self-driving …
Mario Alfonso Prado-Romero, Bardh Prenkaj, Giovanni Stilo
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