Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors
We prove that Bayesian Inference for Latent Space models on graphs is often misspecified and can become overconfident and poorly calibrated.
Riemannian Geometry
Aligning the Unseen in Attributed Graphs: Interplay between Graph Geometry and Node Attributes Manifold
Using a VAE framework, we investigate the potential misalignment between graph geometry and attributes geometry in attributed graphs.
A unified model of horizontal differentiation with general spaces and irrational consumers
This paper offers a novel theoretical framework for horizontal differentiation models, with Riemannian stochastic geometry.