graph-signal-processing

This paper proposes MS-WDRO, a multi-source Wasserstein distributionally robust graph learning framework that fuses heterogeneous sources via Wasserstein barycenter and minimizes worst-case risk for robust graph topology inference, outperforming baselines in graph recovery and diagnostic utility.

This paper applies graph signal processing to analyze how LLMs internally represent numerical sequences during in-context learning, finding that attention-induced token graphs and hidden-state signals show systematic, context-dependent signatures related to input complexity.

This paper proposes a unified denoising diffusion framework for conditional generation of graph signals, introducing a novel U-GNN architecture that extends U-Net to graph-structured data. The method is demonstrated on stock price forecasting and wireless resource allocation tasks.