The field of network analysis and optimization is moving towards relaxed constraints and more efficient algorithms. Researchers are exploring ways to reduce sensor requirements and improve source localization in graphs, while also developing new methods for network monitoring and telemetry. The concept of metric distortion is being re-examined, with a focus on achieving optimal distortion in deterministic tournament rules. Additionally, the security of decentralized gradient descent algorithms against Byzantine agents is being investigated, and new algorithms are being developed for approximate spanning tree counting. Noteworthy papers include: Reducing Sensor Requirements by Relaxing the Network Metric Dimension, which proposes a relaxation of the traditional metric dimension to reduce sensor requirements. MM-INT: Telemetry in Programmable Switches with Multiple Queues using Source-based Multipath Routing, which presents a solution for efficient collection of monitoring information in network monitoring. Metric Distortion for Tournament Voting and Beyond, which establishes a lower bound on the distortion of any deterministic tournament rule and proposes a novel rule guaranteeing low distortion. Security of Distributed Gradient Descent Against Byzantine Agents, which investigates the security of decentralized gradient descent algorithms against Byzantine agents. Approximate Spanning Tree Counting from Uncorrelated Edge Sets, which presents a new algorithm for approximate spanning tree counting with improved runtime.