The field of graph drawing and visualization is moving towards the development of more efficient and effective algorithms for visualizing complex graphs. Researchers are exploring new techniques, such as hybridizing machine learning with metaheuristics, to improve the quality of graph drawings. There is also a focus on developing algorithms that can handle large and dynamic graphs, as well as those that can provide more intuitive and user-friendly visualizations. Noteworthy papers in this area include: An Efficient Hybridization of Graph Representation Learning and Metaheuristics for the Constrained Incremental Graph Drawing Problem, which proposes a hybrid approach that combines graph representation learning with metaheuristics to improve the quality of graph drawings. A Walk on the Wild Side: a Shape-First Methodology for Orthogonal Drawings, which introduces a new methodology that prioritizes minimizing bends in orthogonal drawings, resulting in more geometrically uniform and readable visualizations. An algorithm for accurate and simple-looking metaphorical maps, which presents a multi-fold extension of a force-based algorithm for creating metaphorical maps that balance accuracy and simplicity.