Feb. 12, 2024, 5:41 a.m. | Chirag Chhablani Sarthak Jain Akshay Channesh Ian A. Kash Sourav Medya

cs.LG updates on arXiv.org arxiv.org

Graph Neural Networks (GNNs) have been a powerful tool for node classification tasks in complex networks. However, their decision-making processes remain a black-box to users, making it challenging to understand the reasoning behind their predictions. Counterfactual explanations (CFE) have shown promise in enhancing the interpretability of machine learning models. Prior approaches to compute CFE for GNNS often are learning-based approaches that require training additional graphs. In this paper, we propose a semivalue-based, non-learning approach to generate CFE for node classification …

box classification compute counterfactual cs.ai cs.lg decision game gnns graph graph neural networks interpretability machine machine learning machine learning models making networks neural networks node predictions prior processes reasoning tasks tool

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