<p>We address the challenge of identifying the most influential graph structures in the decisions of Graph Neural Networks (GNNs). To tackle this, we propose a novel approach for evaluating the importance of subgraphs in GNN decisions, with a particular emphasis on calculating Shapley values. Unlike existing methods that impose rigid, predefined constraints on subgraph shapes (e.g., egographs or individual nodes), our approach remains flexible, accommodating arbitrary subgraph structures. Our method begins by analyzing activation rules within the representation spaces generated by the GNN, followed by computing Shapley values for these rules to quantify their contributions to model decisions. Using these Shapley values, we produce both instance-level and model-level explanations, offering deeper insights into the reasoning processes of GNNs. Extensive empirical studies across diverse datasets and comparisons with state-of-the-art methods highlight the effectiveness of our approach in delivering interpretable and robust explanations.</p>