GraphMoRE: Mitigating Topological Heterogeneity via Mixture of
Riemannian Experts
GraphMoRE: Mitigating Topological Heterogeneity via Mixture of
Riemannian Experts
Real-world graphs have inherently complex and diverse topological patterns, known as topological heterogeneity. Most existing works learn graph representation in a single constant curvature space that is insufficient to match the complex geometric shapes, resulting in low-quality embeddings with high distortion. This also constitutes a critical challenge for graph foundation …