SINC: Discovering Global Higher-Order Feature Interactions

Understanding how black-box models combine features to make predictions remains a fundamental challenge in explainable AI (XAI). Existing post-hoc methods primarily focus on individual feature importance, pairwise interactions, or local explanations, making it difficult to recover global interaction structure. We propose Synergy Identification via Network Clustering (SINC), a model-agnostic, post-hoc framework for discovering global higher-order feature interactions in trained black-box models. SINC constructs a sparse interaction network from pairwise Friedman’s H-statistics, identifies communities using Leiden clustering with a Constant Potts Model objective, and ranks their predictive contribution using permutation importance. In doing so, SINC recovers the global feature interaction structure from pairwise interaction measurements. Across controlled synthetic and real-world datasets, we demonstrate that SINC recovers complex feature synergies and effectively identifies uninformative features. Compared with SHAP-based interaction analysis and the Archipelago method, SINC reveals higher-order groupings that are difficult to capture using a pairwise method and yields unified global communities, overcoming the grouping variability inherent in aggregating local explanations. Finally, we show that SINC serves as a diagnostic tool, revealing whether a model has successfully learned the underlying interaction structure of the data.