Reliable and Faithful Generative Explainers for Graph Neural Networks

Reliable and Faithful Generative Explainers for Graph Neural Networks

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Graph neural networks (GNNs) have been effectively implemented in a variety of real-world applications, although their underlying work mechanisms remain a mystery.To unveil this mystery and advocate for trustworthy decision-making, many GNN explainers have bolia outlet gent been proposed.However, existing explainers often face significant challenges, such as the following: (1) explanations being tied to specific instances; (2) limited generalisability to unseen graphs; (3) potential generation of invalid graph structures; and (4) restrictions to particular tasks (e.

g., node classification, graph classification).To address these challenges, we propose a novel explainer, GAN-GNNExplainer, which employs a generator to produce explanations and a discriminator to oversee the generation process, enhancing the reliability of the outputs.

Despite its powell and mahoney bloody mary mix advantages, GAN-GNNExplainer still struggles with generating faithful explanations and underperforms on real-world datasets.To overcome these shortcomings, we introduce ACGAN-GNNExplainer, an approach that improves upon GAN-GNNExplainer by using a more robust discriminator that consistently monitors the generation process, thereby producing explanations that are both reliable and faithful.Extensive experiments on both synthetic and real-world graph datasets demonstrate the superiority of our proposed methods over existing GNN explainers.