<p>Knowledge of protein–protein interactions (PPIs) is essential for understanding cellular function, yet most network analyses focus on binary interactions. Higher-order motifs such as protein triplets can reveal cooperative or competitive relationships but are difficult to distinguish systematically. We present a computational framework to classify protein triplets in the human protein interaction network (hPIN) as cooperative or competitive. The hPIN was embedded in hyperbolic space using the LaBNE + HM algorithm, and a Random Forest classifier was trained on structurally validated triplets from Interactome3D, achieving high accuracy (AUC = 0.88). Angular and hyperbolic distances were key predictive features. Predicted cooperative triplets were enriched in paralogous partners, indicating that paralogs often bind together to a shared protein using non-overlapping surfaces. The model proved to be effective when tested on a new dataset. AlphaFold 3 modeling supported these predictions, showing that cooperative partners bind at distinct sites, while competitive ones overlap. Our results demonstrate the value of hyperbolic geometry for capturing functional organization in protein complexes.</p>