11 citations · 22 across the 7 of their papers we have counts for
3 papers · 1 filter
A Comparative Study of Counterfactual Explainers for Graph Neural Networks Enabling Multiple Types of Graph Edit
Maria Myrto Villia, Filippos Gouidis, Theodore Patkos +1
Counterfactual explanations for graph-structured data seek to determine minimal and realistic modifications required in an input graph to alter a model's prediction to a predefined…
A Comparative Evaluation of Embeddings and LLMs in a Greek Book Publisher Setting - The CUP Dataset
Katerina Papantoniou, Panagiotis Papadakos, Theodore Patkos +3
We present CUP, a Greek book retrieval benchmark consisting of 868 catalog records and 104 expert-annotated queries with graded relevance judgments. We evaluate sparse (BM25), dens…
A Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networks
Maria Myrto Villia, Filippos Gouidis, Theodore Patkos +1
In this study, we propose a novel pipeline for generic, model-agnostic, local-level counterfactual explainability in graph neural networks (GNNs). Although counterfactual explainer…