Attention has become a central inductive bias for deep learning models irrespective of domain. However, increasing theoretical and empirical evidence suggests that Graph Attention Networks (GATs) suffer from the same pathological issues affecting many other Graph Neural Networks (GNNs). First, GAT's features tend to become progressively smoother as more layers are stacked, and second, the model performs poorly in heterophilic graphs. Sheaf Neural Networks (SNNs), a new class of models inspired b…