The field of graph learning and optimization is moving towards more flexible and adaptive methods. Recent developments have focused on improving the efficiency and effectiveness of graph neural networks, with a particular emphasis on incorporating structural information and higher-order relationships. Notable advancements include the development of novel Quality-Diversity algorithms, such as Vector Quantized-Elites, which enable autonomous construction of behavioral space grids without prior task-specific knowledge. Additionally, universal graph structural encoders, like GFSE, have shown great promise in capturing transferable structural patterns across diverse domains. Noteworthy papers include Vector Quantized-Elites, which introduces a novel Quality-Diversity algorithm for unsupervised optimization, and GFSE, a universal graph structural encoder that captures transferable structural patterns across diverse domains.