The field of graph algorithms and streaming models is witnessing significant developments, with a focus on improving the efficiency and accuracy of algorithms for various graph-related problems. Researchers are exploring new approaches to tackle long-standing open questions, such as the edge-weighted matching problem and the online edge coloring problem. Notably, the use of quadratic programming solvers and randomization techniques is leading to breakthroughs in achieving better competitive ratios and sharper thresholds. Additionally, there is a growing interest in designing algorithms for streaming and query models, with a emphasis on approximating degree distributions and constructing directed graphs with preserved properties. These advancements have the potential to impact various applications, from social network analysis to data engineering. Noteworthy papers include: Edge-weighted Matching in the Dark, which presents a novel Quadratic Ranking algorithm that breaks the 1-1/e barrier. Online Edge Coloring: Sharp Thresholds, which establishes sharp thresholds for when greedy algorithms can be surpassed and near-optimal guarantees can be achieved. Towards Tight Bounds for Estimating Degree Distribution in Streaming and Query Models, which provides the first lower bounds for approximating degree distributions in streaming and query models. Constructing and Sampling Directed Graphs with Linearly Rescaled Degree Matrices, which proposes a novel graph sampling algorithm that preserves in-degree and out-degree distributions.