Title: Self-Consistency Improves the Trustworthiness of Self-Interpretable GNNs
Primary area: learning on graphs and other geometries & topologies
Keywords: Self-interpretble GNNs; Trustworthy; Consistency; Faithfulness

Abstract: Graph Neural Networks (GNNs) achieve strong predictive performance but offer limited transparency in their decision-making. Self-Interpretable GNNs (SI-GNNs) address this by generating built-in explanations, yet their training objectives are misaligned with evaluation criteria such as faithfulness. This raises two key questions: (i) can faithfulness be explicitly optimized during training, and (ii) does such optimization genuinely improve explanation quality? We show that faithfulness is intrinsically tied to explanation self-consistency and can therefore be optimized directly. Empirical analysis further reveals that self-inconsistency predominantly occurs on unimportant features, linking it to redundancy-driven explanation inconsistency observed in recent work and suggesting untapped potential for improving explanation quality. Building on these insights, we introduce a simple, model-agnostic self-consistency (SC) training strategy. Without changing architectures or pipelines, SC consistently improves explanation quality across multiple dimensions and benchmarks, offering an effective and scalable pathway to more trustworthy GNN explanations.

TL;DR: We identify the mismatch between SI-GNN training and faithfulness evaluation, show its connection to self-consistency, and propose a simple SC loss that consistently improves explanation quality without architectural changes.