R-MESD: Riemannian Multi Expert Learning for Structural-Semantic Decoupling in Text-Attributed Graphs

Representation learning on text-attributed graphs (TAGs) is crucial for modeling rich node semantics and complex graph structure. Nevertheless, this task faces structural-semantic mismatch arising from divergent modality distributions, as well as heterogeneous local topologies that a single geometric space cannot adequately capture. Existing approaches typically propagate semantic features along the given topology or rely on a fixed representation space, potentially amplifying modality interference and overlooking local geometric diversity. To address these issues, we propose R-MESD, a Riemannian Multi Expert Structural-Semantic Decoupling model in TAGs. Specifically, R-MESD initializes structural positions from Laplacian spectral signals and uses local structural descriptions to route nodes to Hyperbolic and Spherical experts. The expert representations from different manifolds are transformed into a shared latent space within each geometric branch and subsequently fused according to node-level routing weights, followed by cross-geometry attention.Meanwhile, node semantics are represented as tangent vectors anchored at the learned structural positions and further refined through cross-geometry semantic alignment and local tangent-space attention. Extensive experiments on five public benchmarks demonstrate that R-MESD consistently outperforms state-of-the-art baselines.