Graph networks that understand uncertainty, not just numbers

Graph neural networks typically flatten node features into vectors, which works fine for numbers but destroys the geometry of probability distributions. GSNNs treat Gaussian node features (mean + covariance) as structured objects, deriving a new Laplacian operator grounded in sheaf theory that respects their algebraic properties. Experiments on synthetic and real data show this preservation of structure improves learning on tasks where uncertainty matters.