Title: Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networks
Primary area: interpretability and explainable AI
Keywords: Counterfactual Explainability, Trustworthiness, Graph Learning

Abstract: Graph Neural Networks (GNNs) are increasingly adopted in domains like molecular biology and social network analysis, yet their black-box nature hinders interpretability and trust. This is especially problematic in high-stakes applications, such as predicting molecule toxicity, drug discovery, or guiding financial fraud detections, where transparent explanations are essential. Counterfactual explanations - minimal changes that flip a model's prediction - offer a transparent lens into GNNs behavior. In this work, we introduce XPlore, a novel technique that significantly broadens the counterfactual search space. It consists of gradient-guided perturbations to adjacency and node feature matrices. Unlike most prior methods, which focus solely on edge deletions, our approach belongs to the growing class of techniques that optimize edge insertions and node-feature perturbations, here jointly performed under a unified gradient-based framework, enabling a richer and more nuanced exploration of counterfactuals. To quantify both structural and semantic fidelity, we introduce a cosine similarity metric on learned graph embeddings, addressing a key limitation of traditional distance-based metrics, demonstrating that XPlore produces more coherent and minimal counterfactuals. Empirical results on nine real-world and five synthetic benchmarks show up to +56.3% improvement in validity and +52.8% in fidelity over state-of-the-art baselines, while retaining competitive runtime.