The field of graph algorithms and learning is rapidly progressing, with a focus on developing innovative methods for graph analysis, learning, and optimization. Recent developments have centered around improving the efficiency and scalability of graph algorithms, such as those for coreness decomposition, spanner computation, and distance oracles. Additionally, there is a growing interest in applying machine learning techniques to graph-structured data, including graph neural networks and prompt learning. Notably, researchers are exploring new approaches to tackle long-standing problems, such as subgraph counting under differential privacy and estimating average degree in graphs. Furthermore, novel algorithms for signed exponential random graph models and DAG edge deletion have been proposed, demonstrating the field's diversity and depth. Some particularly noteworthy papers include: PLACE, which introduces a graph prompt learning framework for attributed community search, achieving higher F1 scores than state-of-the-art methods. Parallel Batch-Dynamic Coreness Decomposition with Worst-Case Guarantees presents the first parallel batch-dynamic algorithm for coreness decomposition with worst-case update times, outperforming previous algorithms in terms of work bound and runtime. Faster Algorithms for (2k-1)-Stretch Distance Oracles improves upon existing algorithms for constructing distance oracles, achieving subquadratic time construction for every 2 < k < 6.