The field of graph-based machine learning is rapidly advancing, with a focus on developing innovative methods to analyze and predict complex network behavior. Recent research has explored the application of machine learning techniques to evolutionary graph theory, enabling the prediction of cooperation collapse in complex social networks. Additionally, studies have investigated the impact of structural properties on neural network performance, revealing that networks with densely interconnected communities demonstrate enhanced learning capabilities. The development of novel graph-augmented transformer-based models has also shown promise in predicting soccer match outcomes and modeling team dynamics. Furthermore, researchers have introduced biofidelic neural architectures inspired by insect connectomes, which have achieved impressive results in image classification and chess playing tasks. Overall, the field is moving towards more sophisticated and biologically informed models that can capture the complexity of real-world networks. Noteworthy papers include: Machine Learning for Evolutionary Graph Theory, which introduces a machine learning approach to detect abrupt shifts in evolutionary graph theory. Effects of structural properties of neural networks on machine learning performance, which provides insights into the design of more biologically informed neural networks. Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures, which demonstrates the potential of biofidelic neural architectures to support complex cognitive tasks.