Maximum Flow

Maximum flow problems aim to find the largest possible flow through a network, subject to capacity constraints on its edges. Current research focuses on improving the efficiency of classical algorithms like Ford-Fulkerson, often integrating them with machine learning techniques such as graph neural networks to enhance generalization and prediction accuracy in applications like traffic management and resource allocation. These advancements are impacting diverse fields, enabling better solutions for problems ranging from optimizing urban infrastructure (e.g., EV charging station placement, traffic routing) to improving industrial processes (e.g., leakage detection in water networks). The development of efficient parallel algorithms remains an active area of investigation.