Teresa Shang | Searcharxiv

arXiv2026

Wireless Link Scheduling with Graph Signal Diffusion

We solve long-horizon wireless link scheduling with per-link minimum transmission constraints. Fair distribution of resources across links and time steps requires stochastic scheduling, motivating a diffusion framework. Via imitation learning, we train a diffusion model to sample scheduling decisions, conditioning generation on the network topology and constraint levels. The parameterization is achieved through a U-Graph Neural Network (U-GNN) to leverage the relationship between graphs and wireless networks for learning. Our approach achieves near-optimal performance, maximizing sum rate while distributing allocations across different links over time horizons. The U-GNN-based diffusion model generalizes to requirements and network sizes unseen during training, demonstrating the robustness of the approach.

eess.SP↗

arXiv2026

Size-Transferability of Graph Transformers with Convolutional Positional Encodings

Transformers have achieved remarkable success across domains, motivating the rise of Graph Transformers (GTs) as attention-based architectures for graph-structured data. A key design choice in GTs is the use of Graph Neural Network (GNN)-based positional encodings to incorporate structural information. In this work, we study GTs through the lens of manifold limit models for graph sequences and establish a theoretical connection between GTs with GNN positional encodings and Manifold Neural Networks (MNNs). Building on transferability results for GNNs under manifold convergence, we show that GTs inherit transferability guarantees from their positional encodings. In particular, GTs trained on small graphs provably generalize to larger graphs under mild assumptions. We complement our theory with extensive experiments on standard graph benchmarks, demonstrating that GTs exhibit scalable behavior on par with GNNs. To further show the efficiency in a real-world scenario, we implement GTs for shortest path distance estimation over terrains to better illustrate the efficiency of the transferable GTs. Our results provide new insights into the understanding of GTs and suggest practical directions for efficient training of GTs in large-scale settings.

cs.LG↗