The field of graph generation and signal processing is witnessing a significant shift towards geometric and diffusion-based methods. Researchers are increasingly focusing on capturing the underlying structure and geometry of graphs, rather than just their spectral properties. This is evident in the development of novel frameworks that integrate spectral geometry with flow matching, allowing for more efficient and scalable generation of diverse graphs. Additionally, diffusion-based models are being explored for graph signal generation, enabling the creation of synthetic data that preserves essential temporal and spectral properties of real data. Noteworthy papers in this area include Graph Generation with Spectral Geodesic Flow Matching, which proposes a novel framework for graph generation using spectral eigenmaps and geodesic flows. Another notable work is Toward a Unified Geometry Understanding: Riemannian Diffusion Framework for Graph Generation and Prediction, which introduces a Riemannian diffusion model for capturing distinct manifold signatures of complex graph data. RareGraph-Synth is also worth mentioning, as it presents a knowledge-guided diffusion framework for generating realistic yet privacy-preserving synthetic patient trajectories in ultra-rare diseases.