The field of graph learning and optimization is rapidly advancing, with a focus on developing innovative methods for community detection, graph coarsening, and optimization on graph-structured data. Recent research has explored the integration of deep learning techniques with traditional rule-based constraints to improve community search, as well as the development of novel frameworks for graph contrastive learning and global optimization. Additionally, there has been a surge of interest in graph super-resolution, with new approaches being proposed to infer high-resolution graphs from low-resolution counterparts. Notable papers in this area include NCSAC, which introduces a novel approach for neural community search via attribute-augmented conductance, and HiLoMix, which proposes a robust high- and low-frequency graph learning framework for mixing address association. Other noteworthy papers include Dual-Kernel Graph Community Contrastive Learning, which transforms the input graph into a compact network of interconnected node sets, and Gromov-Wasserstein Graph Coarsening, which proposes two algorithms for graph coarsening within the Gromov-Wasserstein geometry.