Active Directed Graph

Active directed graphs represent networks where individual nodes operate autonomously, processing information only from their immediate neighbors, mimicking decentralized biological systems. Current research focuses on improving efficiency and robustness of algorithms operating on these graphs, particularly in applications like node classification and memory modeling, often employing graph convolutional networks (GCNs) and active learning strategies to optimize label usage and accelerate training. These advancements are significant for developing more efficient and scalable machine learning models, as well as providing insights into complex biological systems like the brain.