The field of graph analysis is moving towards more efficient and dynamic methods for handling large-scale graphs and hypergraphs. Recent developments focus on improving spectral sparsification techniques, allowing for faster and more accurate analysis of graph structures. Additionally, there is a growing interest in hypergraph modeling, which enables the representation of higher-order interactions and relationships in complex systems. This has led to the development of new methods for community detection, hyperedge prediction, and inter-hypergraph analysis. These advancements have the potential to significantly impact various fields, including network science, machine learning, and data analysis. Noteworthy papers include: dyGRASS, which presents a dynamic algorithm for spectral sparsification of large undirected graphs, achieving a 10x speedup compared to state-of-the-art methods. Another notable work is the proposal of a model for community and hyperedge inference in multiple hypergraphs, demonstrating strong performance in community detection and hyperedge prediction tasks.